From b621e0296454aa518d42200a0314a3d2c4ce5d38 Mon Sep 17 00:00:00 2001
From: Rui Ribeiro <rui.ribeiro@icloud.com>
Date: Wed, 30 Nov 2022 11:55:14 +0100
Subject: [PATCH] release
---
.gitignore | 10 -
.../Gi_parameters.csv | 0
{SI => Reactions_Parameters}/Gi_reactions.csv | 0
.../Gq_parameters.csv | 0
{SI => Reactions_Parameters}/Gq_reactions.csv | 0
.../Gs_parameters.csv | 0
{SI => Reactions_Parameters}/Gs_reactions.csv | 0
.../OXTR_pathway_parameters.csv | 0
.../OXTR_pathway_reactions.csv | 0
docs/_build/doctrees/apidocs.doctree | Bin 114127 -> 123520 bytes
docs/_build/doctrees/environment.pickle | Bin 27290 -> 27024 bytes
docs/_build/doctrees/index.doctree | Bin 8232 -> 13985 bytes
docs/_build/html/.buildinfo | 2 +-
docs/_build/html/_sources/apidocs.rst.txt | 7 +-
docs/_build/html/_sources/index.rst.txt | 49 +
docs/_build/html/_static/custom.css | 15 +
.../html/_static/documentation_options.js | 2 +-
docs/_build/html/about.html | 2 +-
docs/_build/html/apidocs.html | 508 +++---
docs/_build/html/faq.html | 2 +-
docs/_build/html/genindex.html | 163 +-
docs/_build/html/index.html | 36 +-
docs/_build/html/introduction.html | 2 +-
docs/_build/html/objects.inv | Bin 740 -> 762 bytes
docs/_build/html/py-modindex.html | 24 +-
docs/_build/html/search.html | 2 +-
docs/_build/html/searchindex.js | 2 +-
docs/apidocs.rst | 7 +-
docs/conf.py | 5 +-
ssbtoolkit/Binding.py | 126 ++
ssbtoolkit/Simulation.py | 1277 ++++++++++++++
ssbtoolkit/__init__.py | 1414 ----------------
ssbtoolkit/__init__py | 15 +
ssbtoolkit/__pycache__/Binding.cpython-39.pyc | Bin 0 -> 4055 bytes
.../__pycache__/Simulation.cpython-39.pyc | Bin 0 -> 34045 bytes
ssbtoolkit/__pycache__/Utils.cpython-39.pyc | Bin 13842 -> 13902 bytes
.../__pycache__/__init__.cpython-39.pyc | Bin 39044 -> 39153 bytes
.../pathways/__pycache__/Gi.cpython-39.pyc | Bin 9152 -> 9148 bytes
.../pathways/__pycache__/Gq.cpython-39.pyc | Bin 6707 -> 6703 bytes
.../pathways/__pycache__/Gs.cpython-39.pyc | Bin 8394 -> 8390 bytes
.../__pycache__/OXTR_pathway.cpython-39.pyc | Bin 9023 -> 9019 bytes
.../OXTR_pathway_testing.cpython-39.pyc | Bin 7557 -> 7553 bytes
.../__pycache__/__init__.cpython-39.pyc | Bin 161 -> 157 bytes
ssbtoolkit/ssbmain.py.txt | 1477 -----------------
44 files changed, 1919 insertions(+), 3228 deletions(-)
rename {SI => Reactions_Parameters}/Gi_parameters.csv (100%)
rename {SI => Reactions_Parameters}/Gi_reactions.csv (100%)
rename {SI => Reactions_Parameters}/Gq_parameters.csv (100%)
rename {SI => Reactions_Parameters}/Gq_reactions.csv (100%)
rename {SI => Reactions_Parameters}/Gs_parameters.csv (100%)
rename {SI => Reactions_Parameters}/Gs_reactions.csv (100%)
rename {SI => Reactions_Parameters}/OXTR_pathway_parameters.csv (100%)
rename {SI => Reactions_Parameters}/OXTR_pathway_reactions.csv (100%)
create mode 100644 docs/_build/html/_static/custom.css
create mode 100644 ssbtoolkit/Binding.py
create mode 100644 ssbtoolkit/Simulation.py
delete mode 100644 ssbtoolkit/__init__.py
create mode 100644 ssbtoolkit/__init__py
create mode 100644 ssbtoolkit/__pycache__/Binding.cpython-39.pyc
create mode 100644 ssbtoolkit/__pycache__/Simulation.cpython-39.pyc
delete mode 100644 ssbtoolkit/ssbmain.py.txt
diff --git a/.gitignore b/.gitignore
index 075c900..e69de29 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,10 +0,0 @@
-SSBtoolkit-Tutorial1-DEV.ipynb
-SSBtoolkit-Tutorial2-DEV.ipynb
-SSBtoolkit-Tutorial3A-DEV.ipynb
-SSBtoolkit-Tutorial4-OXTR-DEV.ipynb
-src/lib/__pycache__/
-src/lib/autogrid_reference_files/
-src/lib/autogrids/
-src/lib/ssbtoolkit_BK.py
-.ipynb_checkpoints/
-debugging.txt
diff --git a/SI/Gi_parameters.csv b/Reactions_Parameters/Gi_parameters.csv
similarity index 100%
rename from SI/Gi_parameters.csv
rename to Reactions_Parameters/Gi_parameters.csv
diff --git a/SI/Gi_reactions.csv b/Reactions_Parameters/Gi_reactions.csv
similarity index 100%
rename from SI/Gi_reactions.csv
rename to Reactions_Parameters/Gi_reactions.csv
diff --git a/SI/Gq_parameters.csv b/Reactions_Parameters/Gq_parameters.csv
similarity index 100%
rename from SI/Gq_parameters.csv
rename to Reactions_Parameters/Gq_parameters.csv
diff --git a/SI/Gq_reactions.csv b/Reactions_Parameters/Gq_reactions.csv
similarity index 100%
rename from SI/Gq_reactions.csv
rename to Reactions_Parameters/Gq_reactions.csv
diff --git a/SI/Gs_parameters.csv b/Reactions_Parameters/Gs_parameters.csv
similarity index 100%
rename from SI/Gs_parameters.csv
rename to Reactions_Parameters/Gs_parameters.csv
diff --git a/SI/Gs_reactions.csv b/Reactions_Parameters/Gs_reactions.csv
similarity index 100%
rename from SI/Gs_reactions.csv
rename to Reactions_Parameters/Gs_reactions.csv
diff --git a/SI/OXTR_pathway_parameters.csv b/Reactions_Parameters/OXTR_pathway_parameters.csv
similarity index 100%
rename from SI/OXTR_pathway_parameters.csv
rename to Reactions_Parameters/OXTR_pathway_parameters.csv
diff --git a/SI/OXTR_pathway_reactions.csv b/Reactions_Parameters/OXTR_pathway_reactions.csv
similarity index 100%
rename from SI/OXTR_pathway_reactions.csv
rename to Reactions_Parameters/OXTR_pathway_reactions.csv
diff --git a/docs/_build/doctrees/apidocs.doctree b/docs/_build/doctrees/apidocs.doctree
index 950c7982dccbe876784f1a765790a16807645995..411e468fec93d068e06c10eb2c07fda60af01e46 100644
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diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle
index 7e1e4f608ec182198255e6006d6276e30ec742eb..a455faaafa7b458943156ea1b8570c26912d1cd0 100644
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diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo
index 3b43f5e..0aef523 100644
--- a/docs/_build/html/.buildinfo
+++ b/docs/_build/html/.buildinfo
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# Sphinx build info version 1
# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: b18c64a1de10b91df8b26059330c35f6
tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_build/html/_sources/apidocs.rst.txt b/docs/_build/html/_sources/apidocs.rst.txt
index 5e709c6..9648907 100644
--- a/docs/_build/html/_sources/apidocs.rst.txt
+++ b/docs/_build/html/_sources/apidocs.rst.txt
@@ -1,5 +1,10 @@
API Documentation
=================
+.. automodule:: Binding
+ :members:
+
+.. automodule:: Simulation
+ :members:
-.. automodule:: ssbtoolkit
+.. automodule:: Utils
:members:
diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt
index 9efec59..e304d48 100644
--- a/docs/_build/html/_sources/index.rst.txt
+++ b/docs/_build/html/_sources/index.rst.txt
@@ -46,6 +46,51 @@ protocols.
apidocs
faq
+\
+
+\
+
+Availability and License
+========================
+The source code is freely available at https://github.com/rribeiro-sci/SSBtoolkit under
+the Apache 2.0 license. Tutorial notebooks containing minimal working examples can be found at https://github.com/rribeiro-sci/SSBtoolkit.
+
+\
+
+\
+
+Developed by
+============
+.. image:: https://res.cloudinary.com/djz27k5hg/image/upload/v1637333672/logos/Juelich_logo_100px_ecv2hy.png
+ :width: 200
+
+\
+
+\
+
+Funded by
+=========
+.. image:: https://res.cloudinary.com/djz27k5hg/image/upload/v1637657234/logos/HBP_horizontal_logo_qtcyzn.png
+ :width: 200
+ :alt: Alternative text
+
+Citation |DOI for Citing ssbtoolkit|
+====================================
+
+pyGOMoDo is research software. If you make use of pyGOMoDo in scientific
+publications, please cite it. The BibTeX reference is
+
+::
+
+ @article{ribeiro_ssb_2022,
+ title={{SSB} toolkit: from molecular structure to subcellular signaling pathways.},
+ author={Ribeiro, Rui Pedro and Gossen, Jonas and Rossetti, Giulia and Giorgetti, Alejandro},
+ publisher={bioRxiv},
+ url={https://www.biorxiv.org/content/10.1101/2022.11.08.515595v1},
+ doi={10.1101/2022.11.08.515595},
+ year={2022}
+ }
+
Indices and tables
==================
@@ -53,3 +98,7 @@ Indices and tables
* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`
+
+
+.. |DOI for Citing ssbtoolkit| image:: https://img.shields.io/badge/DOI-10.1016%2Fj.bpj.2015.08.015-blue.svg
+ :target: https://github.com/rribeiro-sci/SSBtoolkit
\ No newline at end of file
diff --git a/docs/_build/html/_static/custom.css b/docs/_build/html/_static/custom.css
new file mode 100644
index 0000000..ff0b629
--- /dev/null
+++ b/docs/_build/html/_static/custom.css
@@ -0,0 +1,15 @@
+.wy-side-nav-search, .wy-nav-top {
+ background: #EFEFEF;
+
+}
+/* Text inside search */
+.wy-side-nav-search input[type=text] {
+ color: #000000;
+}
+
+
+/* Home button */
+.wy-side-nav-search > a {
+ color: #555753;
+ font-size: 25px;
+}
\ No newline at end of file
diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js
index 742ecad..f933e82 100644
--- a/docs/_build/html/_static/documentation_options.js
+++ b/docs/_build/html/_static/documentation_options.js
@@ -1,6 +1,6 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: 'v1',
+ VERSION: 'v1.0.1',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
BUILDER: 'html',
diff --git a/docs/_build/html/about.html b/docs/_build/html/about.html
index cbf3f0b..a20dae4 100644
--- a/docs/_build/html/about.html
+++ b/docs/_build/html/about.html
@@ -4,7 +4,7 @@
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>What is the SSBtoolkit? — SSBtoolkit v1 documentation</title>
+ <title>What is the SSBtoolkit? — SSBtoolkit v1.0.1 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
diff --git a/docs/_build/html/apidocs.html b/docs/_build/html/apidocs.html
index 068c98c..a4eecd1 100644
--- a/docs/_build/html/apidocs.html
+++ b/docs/_build/html/apidocs.html
@@ -4,7 +4,7 @@
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>API Documentation — SSBtoolkit v1 documentation</title>
+ <title>API Documentation — SSBtoolkit v1.0.1 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
@@ -70,15 +70,11 @@
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
<div itemprop="articleBody">
- <section id="module-ssbtoolkit">
-<span id="api-documentation"></span><h1>API Documentation<a class="headerlink" href="#module-ssbtoolkit" title="Permalink to this headline">ïƒ</a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.binding">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ssbtoolkit.</span></span><span class="sig-name descname"><span class="pre">binding</span></span><a class="headerlink" href="#ssbtoolkit.binding" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>This class simulate ligand-target binding curves.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.binding.bind">
-<span class="sig-name descname"><span class="pre">bind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.binding.bind" title="Permalink to this definition">ïƒ</a></dt>
+ <section id="module-Binding">
+<span id="api-documentation"></span><h1>API Documentation<a class="headerlink" href="#module-Binding" title="Permalink to this headline">ïƒ</a></h1>
+<dl class="py function">
+<dt class="sig sig-object py" id="Binding.Bind">
+<span class="sig-prename descclassname"><span class="pre">Binding.</span></span><span class="sig-name descname"><span class="pre">Bind</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Binding.Bind" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Applies an function to calculate the fraction of occupited receptors at equilibrium.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -91,9 +87,15 @@
</dl>
</dd></dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.binding.maxbend">
-<span class="sig-name descname"><span class="pre">maxbend</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.binding.maxbend" title="Permalink to this definition">ïƒ</a></dt>
+<dl class="py function">
+<dt class="sig sig-object py" id="Binding.ShowCurve">
+<span class="sig-prename descclassname"><span class="pre">Binding.</span></span><span class="sig-name descname"><span class="pre">ShowCurve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Binding.ShowCurve" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Plots ligand-target binding curve</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="Binding.SubMaxConcentration">
+<span class="sig-prename descclassname"><span class="pre">Binding.</span></span><span class="sig-name descname"><span class="pre">SubMaxConcentration</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Binding.SubMaxConcentration" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Calculates the maximum bending point of a sigmoid-shaped curve according to the mathod of Sebaugh et al., 2003.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -112,150 +114,13 @@
</div>
</dd></dl>
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.binding.show_curve">
-<span class="sig-name descname"><span class="pre">show_curve</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.binding.show_curve" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Plots ligand-target binding curve</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.convert">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ssbtoolkit.</span></span><span class="sig-name descname"><span class="pre">convert</span></span><a class="headerlink" href="#ssbtoolkit.convert" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Helper functions</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.convert.KineticTempScale">
-<span class="sig-name descname"><span class="pre">KineticTempScale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">koff</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Tu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'K'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.convert.KineticTempScale" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>This function rescales the kinetics constants to a specific temperature.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>kon</strong> – Required (flt): foward kinetic constant</p></li>
-<li><p><strong>koff</strong> – Required (flt): reverse kinetic constant</p></li>
-<li><p><strong>T1</strong> – Required (flt): Initial temperature</p></li>
-<li><p><strong>T2</strong> – Required (flt): Final temperature</p></li>
-</ul>
-</dd>
-<dt class="field-even">Paramter Tu</dt>
-<dd class="field-even"><p>Optional (kwarg str): Temperature Units (kelvin=’K’, celsius=’C’)</p>
-</dd>
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p>(flt, flt)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.convert.microgr2nanomolar">
-<span class="sig-name descname"><span class="pre">microgr2nanomolar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.convert.microgr2nanomolar" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>This function converts micrograms of protein in nanomolar.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>uniprotID</strong> – Required (str)</p></li>
-<li><p><strong>concentration</strong> – Required (int): concentration od protein in micrograms</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>(flt) concentration of protein in nM</p>
-</dd>
-</dl>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>This function will obtain the sequence of the protein from UNIPROT and calculate automatically its molecular mass</p>
-</div>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.get">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ssbtoolkit.</span></span><span class="sig-name descname"><span class="pre">get</span></span><a class="headerlink" href="#ssbtoolkit.get" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Tools to retrive protein information</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.get.gprotein">
-<span class="sig-name descname"><span class="pre">gprotein</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.get.gprotein" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>This function query the SSBtoolkit internal database to extract the G protein associated to GPCR.</p>
-<div class="admonition warning">
-<p class="admonition-title">Warning</p>
-<p>it just works for Human GPCRS!</p>
-</div>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>uniprotID</strong> – Required (str)</p>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>(str)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.get.tauRAMD">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">tauRAMD</span></span><a class="headerlink" href="#ssbtoolkit.get.tauRAMD" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Implementation of the tRAMD method by Kokh et al., 2018.</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.get.tauRAMD.Run">
-<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.get.tauRAMD.Run" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Calulates the residence time of a ligand from RAMD simualtions.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>prefix</strong> – Required (kwarg str): directory path of .dat files</p></li>
-<li><p><strong>dt</strong> – Optional (kwarg flt): MD simulations time step in ns (defaul is 2E-6)</p></li>
-<li><p><strong>softwr</strong> – Optional (kwarg str): software used to perform RAMD simulations: NAMD, GROMACS (default)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Return (str)</dt>
-<dd class="field-even"><p>residence time</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.get.tauRAMD.plotRTdistribuitons">
-<span class="sig-name descname"><span class="pre">plotRTdistribuitons</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.get.tauRAMD.plotRTdistribuitons" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Plots the residence time distributions</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>save</strong> – Optional (kwarg boolean): default False</p></li>
-<li><p><strong>filename</strong> – Optional (kwarg str)</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.get.tauRAMD.plotRTstats">
-<span class="sig-name descname"><span class="pre">plotRTstats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.get.tauRAMD.plotRTstats" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Plots the residence time statistics</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>save</strong> – Optional (kwarg boolean): default False</p></li>
-<li><p><strong>filename</strong> – Optional (kwarg str)</p></li>
-</ul>
-</dd>
-</dl>
-</dd></dl>
-
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ssbtoolkit.</span></span><span class="sig-name descname"><span class="pre">simulation</span></span><a class="headerlink" href="#ssbtoolkit.simulation" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>This class simulates the mathematical models of the signaling pathways.</p>
-<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">activation</span></span><a class="headerlink" href="#ssbtoolkit.simulation.activation" title="Permalink to this definition">ïƒ</a></dt>
+<span class="target" id="module-Simulation"></span><dl class="py class">
+<dt class="sig sig-object py" id="Simulation.Activation">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Simulation.</span></span><span class="sig-name descname"><span class="pre">Activation</span></span><a class="headerlink" href="#Simulation.Activation" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Simulation of the activation of signaling pathways (i.e. activation by agonists)</p>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.Analysis">
-<span class="sig-name descname"><span class="pre">Analysis</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.Analysis" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.Analysis">
+<span class="sig-name descname"><span class="pre">Analysis</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.Analysis" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>This function calculates the dose-response effect.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
@@ -265,14 +130,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.Curve">
-<span class="sig-name descname"><span class="pre">Curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.Curve" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Plots the dose-response curve.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.PathwayParameters">
-<span class="sig-name descname"><span class="pre">PathwayParameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.PathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.PathwayParameters">
+<span class="sig-name descname"><span class="pre">PathwayParameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.PathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Display table with default pathway parameters.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
@@ -281,8 +140,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.PathwayParametersToCSV">
-<span class="sig-name descname"><span class="pre">PathwayParametersToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.PathwayParametersToCSV" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.PathwayParametersToCSV">
+<span class="sig-name descname"><span class="pre">PathwayParametersToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.PathwayParametersToCSV" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Export pathway parameters into CSV format.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -292,14 +151,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.Potency">
-<span class="sig-name descname"><span class="pre">Potency</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.Potency" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Return the potency values as a pandas DataFrame.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.PotencyToCSV">
-<span class="sig-name descname"><span class="pre">PotencyToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.PotencyToCSV" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.PotencyToCSV">
+<span class="sig-name descname"><span class="pre">PotencyToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.PotencyToCSV" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Exports the potency values into csv format.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -309,26 +162,26 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.PotencyToDict">
-<span class="sig-name descname"><span class="pre">PotencyToDict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.PotencyToDict" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.PotencyToDict">
+<span class="sig-name descname"><span class="pre">PotencyToDict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.PotencyToDict" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Convert potencies into a dictionary.</p>
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.Reactions">
-<span class="sig-name descname"><span class="pre">Reactions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.Reactions" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.Reactions">
+<span class="sig-name descname"><span class="pre">Reactions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.Reactions" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Display pathway reactions.</p>
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.Run">
-<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.Run" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.Run">
+<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.Run" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>This function runs the pathway simulation and returns the raw simulation data.</p>
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.SetSimulationParameters">
-<span class="sig-name descname"><span class="pre">SetSimulationParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.SetSimulationParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.SetSimulationParameters">
+<span class="sig-name descname"><span class="pre">SetSimulationParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.SetSimulationParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
@@ -350,8 +203,20 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.activation.UserPathwayParameters">
-<span class="sig-name descname"><span class="pre">UserPathwayParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.activation.UserPathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Activation.ShowCurve">
+<span class="sig-name descname"><span class="pre">ShowCurve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.ShowCurve" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Plots the dose-response curve.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="Simulation.Activation.ShowPotency">
+<span class="sig-name descname"><span class="pre">ShowPotency</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.ShowPotency" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Return the potency values as a pandas DataFrame.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="Simulation.Activation.UserPathwayParameters">
+<span class="sig-name descname"><span class="pre">UserPathwayParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Activation.UserPathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Import user pathway parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -363,16 +228,16 @@
</dd></dl>
<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">fitModel</span></span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.FitModel">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Simulation.</span></span><span class="sig-name descname"><span class="pre">FitModel</span></span><a class="headerlink" href="#Simulation.FitModel" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Fit a model to experimental data.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This class was developed to reproduce data from a specific experimental setup. Please see tutorial 4 (OXTR pathay). Use carefully!</p>
</div>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.PathwayParameters">
-<span class="sig-name descname"><span class="pre">PathwayParameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.PathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.FitModel.PathwayParameters">
+<span class="sig-name descname"><span class="pre">PathwayParameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.PathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Display table with default pathway parameters.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
@@ -381,8 +246,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.PathwayParametersToCSV">
-<span class="sig-name descname"><span class="pre">PathwayParametersToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.PathwayParametersToCSV" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.FitModel.PathwayParametersToCSV">
+<span class="sig-name descname"><span class="pre">PathwayParametersToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.PathwayParametersToCSV" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Export pathway parameters into CSV format.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -392,14 +257,20 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.Reactions">
-<span class="sig-name descname"><span class="pre">Reactions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.Reactions" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.FitModel.PlotIterations">
+<span class="sig-name descname"><span class="pre">PlotIterations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.PlotIterations" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Plot iterations.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="Simulation.FitModel.Reactions">
+<span class="sig-name descname"><span class="pre">Reactions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.Reactions" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Display pathway reactions.</p>
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.Run">
-<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.Run" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.FitModel.Run">
+<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.Run" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Fits of the model to experimental data.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -416,8 +287,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.SetSimulationParameters">
-<span class="sig-name descname"><span class="pre">SetSimulationParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.SetSimulationParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.FitModel.SetSimulationParameters">
+<span class="sig-name descname"><span class="pre">SetSimulationParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.SetSimulationParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
@@ -435,19 +306,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.UserPathwayParameters">
-<span class="sig-name descname"><span class="pre">UserPathwayParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.UserPathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Import user pathway parameters.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>path</strong> – Required (kwarg str): directory path</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.plotCurves">
-<span class="sig-name descname"><span class="pre">plotCurves</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.plotCurves" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.FitModel.ShowGraphs">
+<span class="sig-name descname"><span class="pre">ShowGraphs</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.ShowGraphs" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Plot the amount of obeservable in function of time, Amplitude, Area Under the Curve, and Full Width at Half Maximum.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -460,20 +320,25 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.fitModel.plotIterations">
-<span class="sig-name descname"><span class="pre">plotIterations</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.fitModel.plotIterations" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Plot iterations.</p>
+<dt class="sig sig-object py" id="Simulation.FitModel.UserPathwayParameters">
+<span class="sig-name descname"><span class="pre">UserPathwayParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.FitModel.UserPathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Import user pathway parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>path</strong> – Required (kwarg str): directory path</p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
<dl class="py class">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">inhibition</span></span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Simulation.</span></span><span class="sig-name descname"><span class="pre">Inhibition</span></span><a class="headerlink" href="#Simulation.Inhibition" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Simulation of the inhibition of signaling pathways (i.e. inhibition by antagonists).</p>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.Analysis">
-<span class="sig-name descname"><span class="pre">Analysis</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.Analysis" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.Analysis">
+<span class="sig-name descname"><span class="pre">Analysis</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.Analysis" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>This function calculates the dose-response effect.</p>
<dl class="field-list simple">
<dt class="field-odd">Returns</dt>
@@ -483,14 +348,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.Curve">
-<span class="sig-name descname"><span class="pre">Curve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.Curve" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Plot the dose-response curve.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.PathwayParameters">
-<span class="sig-name descname"><span class="pre">PathwayParameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.PathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.PathwayParameters">
+<span class="sig-name descname"><span class="pre">PathwayParameters</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.PathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Display table with default pathway parameters.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
@@ -499,8 +358,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.PathwayParametersToCSV">
-<span class="sig-name descname"><span class="pre">PathwayParametersToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.PathwayParametersToCSV" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.PathwayParametersToCSV">
+<span class="sig-name descname"><span class="pre">PathwayParametersToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.PathwayParametersToCSV" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Export pathway parameters into CSV format.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -510,14 +369,8 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.Potency">
-<span class="sig-name descname"><span class="pre">Potency</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.Potency" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Return the potency values as a pandas DataFrame.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.PotencyToCSV">
-<span class="sig-name descname"><span class="pre">PotencyToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.PotencyToCSV" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.PotencyToCSV">
+<span class="sig-name descname"><span class="pre">PotencyToCSV</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.PotencyToCSV" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Exports the potency values into csv format.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -527,26 +380,26 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.PotencyToDict">
-<span class="sig-name descname"><span class="pre">PotencyToDict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.PotencyToDict" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.PotencyToDict">
+<span class="sig-name descname"><span class="pre">PotencyToDict</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.PotencyToDict" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Convert potencies into a dictionary.</p>
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.Reactions">
-<span class="sig-name descname"><span class="pre">Reactions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.Reactions" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.Reactions">
+<span class="sig-name descname"><span class="pre">Reactions</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.Reactions" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Display pathway reactions.</p>
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.Run">
-<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.Run" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.Run">
+<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.Run" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>This function runs the pathway simulation and returns the raw simulation data.</p>
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.SetSimulationParameters">
-<span class="sig-name descname"><span class="pre">SetSimulationParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.SetSimulationParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.SetSimulationParameters">
+<span class="sig-name descname"><span class="pre">SetSimulationParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.SetSimulationParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
@@ -574,8 +427,20 @@
</dd></dl>
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.UserPathwayParameters">
-<span class="sig-name descname"><span class="pre">UserPathwayParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.UserPathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
+<dt class="sig sig-object py" id="Simulation.Inhibition.ShowCurve">
+<span class="sig-name descname"><span class="pre">ShowCurve</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.ShowCurve" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Plot the dose-response curve.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="Simulation.Inhibition.ShowPotency">
+<span class="sig-name descname"><span class="pre">ShowPotency</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.ShowPotency" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Return the potency values as a pandas DataFrame.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="Simulation.Inhibition.UserPathwayParameters">
+<span class="sig-name descname"><span class="pre">UserPathwayParameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Simulation.Inhibition.UserPathwayParameters" title="Permalink to this definition">ïƒ</a></dt>
<dd><p>Import user pathway parameters.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
@@ -584,12 +449,163 @@
</dl>
</dd></dl>
+</dd></dl>
+
+<dl class="py data">
+<dt class="sig sig-object py" id="Simulation.pathways_path">
+<span class="sig-prename descclassname"><span class="pre">Simulation.</span></span><span class="sig-name descname"><span class="pre">pathways_path</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'/home/rribeiro/Projects/SSBtoolkit/ssbtoolkit/pathways'</span></em><a class="headerlink" href="#Simulation.pathways_path" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Module to simulate the mathematical models of the signaling pathways.</p>
+</dd></dl>
+
+<span class="target" id="module-Utils"></span><dl class="py function">
+<dt class="sig sig-object py" id="Utils.CalcOccupancy">
+<span class="sig-prename descclassname"><span class="pre">Utils.</span></span><span class="sig-name descname"><span class="pre">CalcOccupancy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">receptor_conc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">agonist_conc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">antagonist_conc</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pkd_agonist</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pkd_antagonist</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.CalcOccupancy" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>This function calculates the fraction of occupited receptors at equilibrium.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>receptor_conc</strong> – Required (int): concentration of the receptor</p></li>
+<li><p><strong>agonists_conc</strong> – Required (int): concentration of the agonist</p></li>
+<li><p><strong>antagonists_conc</strong> – Required (int): concentration of the antagonists (0 if antagonist shoul not be considered)</p></li>
+<li><p><strong>pkd_agonist</strong> – Required (int): pKd of agonist</p></li>
+<li><p><strong>pkd_antagonist</strong> – Required (int): pKd of antagonists (if antagonist shoul not be considered)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return int</dt>
+<dd class="field-even"><p>fraction of occupied receptors in the equilibrium</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="Utils.GetGProtein">
+<span class="sig-prename descclassname"><span class="pre">Utils.</span></span><span class="sig-name descname"><span class="pre">GetGProtein</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniprotID</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.GetGProtein" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>This function query the SSBtoolkit internal database to extract the G protein associated to GPCR.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>it just works for Human GPCRS!</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><p><strong>uniprotID</strong> – Required (str)</p>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>(str)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="Utils.KineticTempScale">
+<span class="sig-prename descclassname"><span class="pre">Utils.</span></span><span class="sig-name descname"><span class="pre">KineticTempScale</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">kon</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">koff</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">T2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Tu</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'K'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.KineticTempScale" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>This function rescales the kinetics constants to a specific temperature.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>kon</strong> – Required (flt): foward kinetic constant</p></li>
+<li><p><strong>koff</strong> – Required (flt): reverse kinetic constant</p></li>
+<li><p><strong>T1</strong> – Required (flt): Initial temperature</p></li>
+<li><p><strong>T2</strong> – Required (flt): Final temperature</p></li>
+</ul>
+</dd>
+<dt class="field-even">Paramter Tu</dt>
+<dd class="field-even"><p>Optional (kwarg str): Temperature Units (kelvin=’K’, celsius=’C’)</p>
+</dd>
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>(flt, flt)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="Utils.MicrogramsToNanomolar">
+<span class="sig-prename descclassname"><span class="pre">Utils.</span></span><span class="sig-name descname"><span class="pre">MicrogramsToNanomolar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">uniprotID</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">concentration</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.MicrogramsToNanomolar" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>This function converts micrograms of protein in nanomolar.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>uniprotID</strong> – Required (str)</p></li>
+<li><p><strong>concentration</strong> – Required (int): concentration od protein in micrograms</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns</dt>
+<dd class="field-even"><p>(flt) concentration of protein in nM</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This function will obtain the sequence of the protein from UNIPROT and calculate automatically its molecular mass</p>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="Utils.PrintProgressBar">
+<span class="sig-prename descclassname"><span class="pre">Utils.</span></span><span class="sig-name descname"><span class="pre">PrintProgressBar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">iteration</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">total</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">suffix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">decimals</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">length</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fill</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'â–ˆ'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">printEnd</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'\r'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.PrintProgressBar" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Call in a loop to create terminal progress bar</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>iteration:Required</strong> – current iteration (Int)</p></li>
+<li><p><strong>total</strong> – Required: total iterations (Int)</p></li>
+<li><p><strong>prefix</strong> – Optional: prefix string (Str)</p></li>
+<li><p><strong>suffix</strong> – Optional: suffix string (Str)</p></li>
+<li><p><strong>decimals</strong> – Optional: positive number of decimals in percent complete (Int)</p></li>
+<li><p><strong>length</strong> – Optional: character length of bar (Int)</p></li>
+<li><p><strong>fill</strong> – Optional: bar fill character (Str)</p></li>
+<li><p><strong>printEnd</strong> – Optional: end character (Str)</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="Utils.tauRAMD">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Utils.</span></span><span class="sig-name descname"><span class="pre">tauRAMD</span></span><a class="headerlink" href="#Utils.tauRAMD" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Implementation of the tRAMD method by Kokh et al., 2018.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="Utils.tauRAMD.PlotRTDistribuitons">
+<span class="sig-name descname"><span class="pre">PlotRTDistribuitons</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.tauRAMD.PlotRTDistribuitons" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Plots the residence time distributions</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>save</strong> – Optional (kwarg boolean): default False</p></li>
+<li><p><strong>filename</strong> – Optional (kwarg str)</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
<dl class="py method">
-<dt class="sig sig-object py" id="ssbtoolkit.simulation.inhibition.constants">
-<span class="sig-name descname"><span class="pre">constants</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#ssbtoolkit.simulation.inhibition.constants" title="Permalink to this definition">ïƒ</a></dt>
-<dd><p>Returns the potency values.</p>
+<dt class="sig sig-object py" id="Utils.tauRAMD.PlotRTStats">
+<span class="sig-name descname"><span class="pre">PlotRTStats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">save</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.tauRAMD.PlotRTStats" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Plots the residence time statistics</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>save</strong> – Optional (kwarg boolean): default False</p></li>
+<li><p><strong>filename</strong> – Optional (kwarg str)</p></li>
+</ul>
+</dd>
+</dl>
</dd></dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="Utils.tauRAMD.Run">
+<span class="sig-name descname"><span class="pre">Run</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Utils.tauRAMD.Run" title="Permalink to this definition">ïƒ</a></dt>
+<dd><p>Calulates the residence time of a ligand from RAMD simualtions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>prefix</strong> – Required (kwarg str): directory path of .dat files</p></li>
+<li><p><strong>dt</strong> – Optional (kwarg flt): MD simulations time step in ns (defaul is 2E-6)</p></li>
+<li><p><strong>softwr</strong> – Optional (kwarg str): software used to perform RAMD simulations: NAMD, GROMACS (default)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return (str)</dt>
+<dd class="field-even"><p>residence time</p>
+</dd>
+</dl>
</dd></dl>
</dd></dl>
diff --git a/docs/_build/html/faq.html b/docs/_build/html/faq.html
index 45eff2f..33356e4 100644
--- a/docs/_build/html/faq.html
+++ b/docs/_build/html/faq.html
@@ -4,7 +4,7 @@
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Frequently Asked Questions — SSBtoolkit v1 documentation</title>
+ <title>Frequently Asked Questions — SSBtoolkit v1.0.1 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
index dc0e12d..e3142a3 100644
--- a/docs/_build/html/genindex.html
+++ b/docs/_build/html/genindex.html
@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Index — SSBtoolkit v1 documentation</title>
+ <title>Index — SSBtoolkit v1.0.1 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
@@ -73,22 +73,29 @@
<a href="#A"><strong>A</strong></a>
| <a href="#B"><strong>B</strong></a>
| <a href="#C"><strong>C</strong></a>
+ | <a href="#F"><strong>F</strong></a>
| <a href="#G"><strong>G</strong></a>
+ | <a href="#I"><strong>I</strong></a>
| <a href="#K"><strong>K</strong></a>
| <a href="#M"><strong>M</strong></a>
| <a href="#P"><strong>P</strong></a>
| <a href="#R"><strong>R</strong></a>
| <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
| <a href="#U"><strong>U</strong></a>
</div>
<h2 id="A">A</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.Analysis">Analysis() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation">Activation (class in Simulation)</a>
+</li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="apidocs.html#Simulation.Activation.Analysis">Analysis() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.Analysis">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.Analysis">(Simulation.Inhibition method)</a>
</li>
</ul></li>
</ul></td>
@@ -97,43 +104,48 @@
<h2 id="B">B</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.binding.bind">bind() (ssbtoolkit.binding method)</a>
+ <li><a href="apidocs.html#Binding.Bind">Bind() (in module Binding)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.binding">binding (class in ssbtoolkit)</a>
+ <li>
+ Binding
+
+ <ul>
+ <li><a href="apidocs.html#module-Binding">module</a>
</li>
+ </ul></li>
</ul></td>
</tr></table>
<h2 id="C">C</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.constants">constants() (ssbtoolkit.simulation.inhibition method)</a>
-</li>
- <li><a href="apidocs.html#ssbtoolkit.convert">convert (class in ssbtoolkit)</a>
+ <li><a href="apidocs.html#Utils.CalcOccupancy">CalcOccupancy() (in module Utils)</a>
</li>
</ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.Curve">Curve() (ssbtoolkit.simulation.activation method)</a>
+</tr></table>
- <ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.Curve">(ssbtoolkit.simulation.inhibition method)</a>
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="apidocs.html#Simulation.FitModel">FitModel (class in Simulation)</a>
</li>
- </ul></li>
</ul></td>
</tr></table>
<h2 id="G">G</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.get">get (class in ssbtoolkit)</a>
+ <li><a href="apidocs.html#Utils.GetGProtein">GetGProtein() (in module Utils)</a>
</li>
</ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.get.tauRAMD">get.tauRAMD (class in ssbtoolkit)</a>
-</li>
- <li><a href="apidocs.html#ssbtoolkit.get.gprotein">gprotein() (ssbtoolkit.get method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition">Inhibition (class in Simulation)</a>
</li>
</ul></td>
</tr></table>
@@ -141,7 +153,7 @@
<h2 id="K">K</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.convert.KineticTempScale">KineticTempScale() (ssbtoolkit.convert method)</a>
+ <li><a href="apidocs.html#Utils.KineticTempScale">KineticTempScale() (in module Utils)</a>
</li>
</ul></td>
</tr></table>
@@ -149,17 +161,17 @@
<h2 id="M">M</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.binding.maxbend">maxbend() (ssbtoolkit.binding method)</a>
-</li>
- <li><a href="apidocs.html#ssbtoolkit.convert.microgr2nanomolar">microgr2nanomolar() (ssbtoolkit.convert method)</a>
+ <li><a href="apidocs.html#Utils.MicrogramsToNanomolar">MicrogramsToNanomolar() (in module Utils)</a>
</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
<li>
module
<ul>
- <li><a href="apidocs.html#module-ssbtoolkit">ssbtoolkit</a>
+ <li><a href="apidocs.html#module-Binding">Binding</a>
+</li>
+ <li><a href="apidocs.html#module-Simulation">Simulation</a>
+</li>
+ <li><a href="apidocs.html#module-Utils">Utils</a>
</li>
</ul></li>
</ul></td>
@@ -168,74 +180,70 @@
<h2 id="P">P</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.PathwayParameters">PathwayParameters() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.PathwayParameters">PathwayParameters() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.PathwayParameters">(ssbtoolkit.simulation.fitModel method)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.PathwayParameters">(Simulation.FitModel method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.PathwayParameters">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.PathwayParameters">(Simulation.Inhibition method)</a>
</li>
</ul></li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.PathwayParametersToCSV">PathwayParametersToCSV() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.PathwayParametersToCSV">PathwayParametersToCSV() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.PathwayParametersToCSV">(ssbtoolkit.simulation.fitModel method)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.PathwayParametersToCSV">(Simulation.FitModel method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.PathwayParametersToCSV">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.PathwayParametersToCSV">(Simulation.Inhibition method)</a>
</li>
</ul></li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.plotCurves">plotCurves() (ssbtoolkit.simulation.fitModel method)</a>
-</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.plotIterations">plotIterations() (ssbtoolkit.simulation.fitModel method)</a>
+ <li><a href="apidocs.html#Simulation.pathways_path">pathways_path (in module Simulation)</a>
</li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.get.tauRAMD.plotRTdistribuitons">plotRTdistribuitons() (ssbtoolkit.get.tauRAMD method)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.PlotIterations">PlotIterations() (Simulation.FitModel method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.get.tauRAMD.plotRTstats">plotRTstats() (ssbtoolkit.get.tauRAMD method)</a>
+ <li><a href="apidocs.html#Utils.tauRAMD.PlotRTDistribuitons">PlotRTDistribuitons() (Utils.tauRAMD method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.Potency">Potency() (ssbtoolkit.simulation.activation method)</a>
-
- <ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.Potency">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Utils.tauRAMD.PlotRTStats">PlotRTStats() (Utils.tauRAMD method)</a>
</li>
- </ul></li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.PotencyToCSV">PotencyToCSV() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.PotencyToCSV">PotencyToCSV() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.PotencyToCSV">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.PotencyToCSV">(Simulation.Inhibition method)</a>
</li>
</ul></li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.PotencyToDict">PotencyToDict() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.PotencyToDict">PotencyToDict() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.PotencyToDict">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.PotencyToDict">(Simulation.Inhibition method)</a>
</li>
</ul></li>
+ <li><a href="apidocs.html#Utils.PrintProgressBar">PrintProgressBar() (in module Utils)</a>
+</li>
</ul></td>
</tr></table>
<h2 id="R">R</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.Reactions">Reactions() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.Reactions">Reactions() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.Reactions">(ssbtoolkit.simulation.fitModel method)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.Reactions">(Simulation.FitModel method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.Reactions">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.Reactions">(Simulation.Inhibition method)</a>
</li>
</ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.get.tauRAMD.Run">Run() (ssbtoolkit.get.tauRAMD method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.Run">Run() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.Run">(ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.Run">(Simulation.FitModel method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.Run">(ssbtoolkit.simulation.fitModel method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.Run">(Simulation.Inhibition method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.Run">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Utils.tauRAMD.Run">(Utils.tauRAMD method)</a>
</li>
</ul></li>
</ul></td>
@@ -244,45 +252,70 @@
<h2 id="S">S</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.SetSimulationParameters">SetSimulationParameters() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.SetSimulationParameters">SetSimulationParameters() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.SetSimulationParameters">(ssbtoolkit.simulation.fitModel method)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.SetSimulationParameters">(Simulation.FitModel method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.SetSimulationParameters">(ssbtoolkit.simulation.inhibition method)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.SetSimulationParameters">(Simulation.Inhibition method)</a>
</li>
</ul></li>
- <li><a href="apidocs.html#ssbtoolkit.binding.show_curve">show_curve() (ssbtoolkit.binding method)</a>
+ <li><a href="apidocs.html#Binding.ShowCurve">ShowCurve() (in module Binding)</a>
+
+ <ul>
+ <li><a href="apidocs.html#Simulation.Activation.ShowCurve">(Simulation.Activation method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation">simulation (class in ssbtoolkit)</a>
+ <li><a href="apidocs.html#Simulation.Inhibition.ShowCurve">(Simulation.Inhibition method)</a>
</li>
+ </ul></li>
</ul></td>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation">simulation.activation (class in ssbtoolkit)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.ShowGraphs">ShowGraphs() (Simulation.FitModel method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel">simulation.fitModel (class in ssbtoolkit)</a>
-</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition">simulation.inhibition (class in ssbtoolkit)</a>
+ <li><a href="apidocs.html#Simulation.Activation.ShowPotency">ShowPotency() (Simulation.Activation method)</a>
+
+ <ul>
+ <li><a href="apidocs.html#Simulation.Inhibition.ShowPotency">(Simulation.Inhibition method)</a>
</li>
+ </ul></li>
<li>
- ssbtoolkit
+ Simulation
<ul>
- <li><a href="apidocs.html#module-ssbtoolkit">module</a>
+ <li><a href="apidocs.html#module-Simulation">module</a>
</li>
</ul></li>
+ <li><a href="apidocs.html#Binding.SubMaxConcentration">SubMaxConcentration() (in module Binding)</a>
+</li>
+ </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li><a href="apidocs.html#Utils.tauRAMD">tauRAMD (class in Utils)</a>
+</li>
</ul></td>
</tr></table>
<h2 id="U">U</h2>
<table style="width: 100%" class="indextable genindextable"><tr>
<td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.activation.UserPathwayParameters">UserPathwayParameters() (ssbtoolkit.simulation.activation method)</a>
+ <li><a href="apidocs.html#Simulation.Activation.UserPathwayParameters">UserPathwayParameters() (Simulation.Activation method)</a>
<ul>
- <li><a href="apidocs.html#ssbtoolkit.simulation.fitModel.UserPathwayParameters">(ssbtoolkit.simulation.fitModel method)</a>
+ <li><a href="apidocs.html#Simulation.FitModel.UserPathwayParameters">(Simulation.FitModel method)</a>
+</li>
+ <li><a href="apidocs.html#Simulation.Inhibition.UserPathwayParameters">(Simulation.Inhibition method)</a>
</li>
- <li><a href="apidocs.html#ssbtoolkit.simulation.inhibition.UserPathwayParameters">(ssbtoolkit.simulation.inhibition method)</a>
+ </ul></li>
+ </ul></td>
+ <td style="width: 33%; vertical-align: top;"><ul>
+ <li>
+ Utils
+
+ <ul>
+ <li><a href="apidocs.html#module-Utils">module</a>
</li>
</ul></li>
</ul></td>
diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html
index 1f903ed..fc6512e 100644
--- a/docs/_build/html/index.html
+++ b/docs/_build/html/index.html
@@ -4,7 +4,7 @@
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Welcome to SSBtoolkit’s documentation! — SSBtoolkit v1 documentation</title>
+ <title>Welcome to SSBtoolkit’s documentation! — SSBtoolkit v1.0.1 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
@@ -102,6 +102,40 @@ protocols.</p>
</li>
</ul>
</div>
+<p></p>
+<p></p>
+</section>
+<section id="availability-and-license">
+<h1>Availability and License<a class="headerlink" href="#availability-and-license" title="Permalink to this headline">ïƒ</a></h1>
+<p>The source code is freely available at <a class="reference external" href="https://github.com/rribeiro-sci/SSBtoolkit">https://github.com/rribeiro-sci/SSBtoolkit</a> under
+the Apache 2.0 license. Tutorial notebooks containing minimal working examples can be found at <a class="reference external" href="https://github.com/rribeiro-sci/SSBtoolkit">https://github.com/rribeiro-sci/SSBtoolkit</a>.</p>
+<p></p>
+<p></p>
+</section>
+<section id="developed-by">
+<h1>Developed by<a class="headerlink" href="#developed-by" title="Permalink to this headline">ïƒ</a></h1>
+<a class="reference internal image-reference" href="https://res.cloudinary.com/djz27k5hg/image/upload/v1637333672/logos/Juelich_logo_100px_ecv2hy.png"><img alt="https://res.cloudinary.com/djz27k5hg/image/upload/v1637333672/logos/Juelich_logo_100px_ecv2hy.png" src="https://res.cloudinary.com/djz27k5hg/image/upload/v1637333672/logos/Juelich_logo_100px_ecv2hy.png" style="width: 200px;" /></a>
+<p></p>
+<p></p>
+</section>
+<section id="funded-by">
+<h1>Funded by<a class="headerlink" href="#funded-by" title="Permalink to this headline">ïƒ</a></h1>
+<a class="reference internal image-reference" href="https://res.cloudinary.com/djz27k5hg/image/upload/v1637657234/logos/HBP_horizontal_logo_qtcyzn.png"><img alt="Alternative text" src="https://res.cloudinary.com/djz27k5hg/image/upload/v1637657234/logos/HBP_horizontal_logo_qtcyzn.png" style="width: 200px;" /></a>
+</section>
+<section id="citation-doi-for-citing-ssbtoolkit">
+<h1>Citation <a class="reference external" href="https://github.com/rribeiro-sci/SSBtoolkit"><img alt="DOI for Citing ssbtoolkit" src="https://img.shields.io/badge/DOI-10.1016%2Fj.bpj.2015.08.015-blue.svg" /></a><a class="headerlink" href="#citation-doi-for-citing-ssbtoolkit" title="Permalink to this headline">ïƒ</a></h1>
+<p>pyGOMoDo is research software. If you make use of pyGOMoDo in scientific
+publications, please cite it. The BibTeX reference is</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="nd">@article</span><span class="p">{</span><span class="n">ribeiro_ssb_2022</span><span class="p">,</span>
+ <span class="n">title</span><span class="o">=</span><span class="p">{{</span><span class="n">SSB</span><span class="p">}</span> <span class="n">toolkit</span><span class="p">:</span> <span class="kn">from</span> <span class="nn">molecular</span> <span class="n">structure</span> <span class="n">to</span> <span class="n">subcellular</span> <span class="n">signaling</span> <span class="n">pathways</span><span class="o">.</span><span class="p">},</span>
+ <span class="n">author</span><span class="o">=</span><span class="p">{</span><span class="n">Ribeiro</span><span class="p">,</span> <span class="n">Rui</span> <span class="n">Pedro</span> <span class="ow">and</span> <span class="n">Gossen</span><span class="p">,</span> <span class="n">Jonas</span> <span class="ow">and</span> <span class="n">Rossetti</span><span class="p">,</span> <span class="n">Giulia</span> <span class="ow">and</span> <span class="n">Giorgetti</span><span class="p">,</span> <span class="n">Alejandro</span><span class="p">},</span>
+ <span class="n">publisher</span><span class="o">=</span><span class="p">{</span><span class="n">bioRxiv</span><span class="p">},</span>
+ <span class="n">url</span><span class="o">=</span><span class="p">{</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">www</span><span class="o">.</span><span class="n">biorxiv</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="n">content</span><span class="o">/</span><span class="mf">10.1101</span><span class="o">/</span><span class="mf">2022.11.08.515595</span><span class="n">v1</span><span class="p">},</span>
+ <span class="n">doi</span><span class="o">=</span><span class="p">{</span><span class="mf">10.1101</span><span class="o">/</span><span class="mf">2022.11.08.515595</span><span class="p">},</span>
+ <span class="n">year</span><span class="o">=</span><span class="p">{</span><span class="mi">2022</span><span class="p">}</span>
+<span class="p">}</span>
+</pre></div>
+</div>
</section>
<section id="indices-and-tables">
<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">ïƒ</a></h1>
diff --git a/docs/_build/html/introduction.html b/docs/_build/html/introduction.html
index b11de8e..64f5e97 100644
--- a/docs/_build/html/introduction.html
+++ b/docs/_build/html/introduction.html
@@ -4,7 +4,7 @@
<meta charset="utf-8" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Introduction to Structure Systems Biology — SSBtoolkit v1 documentation</title>
+ <title>Introduction to Structure Systems Biology — SSBtoolkit v1.0.1 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv
index 0c21759fdd3c5c2c947181620ffc433818551dc0..a3b541ea75cebc5e5b3e2462f8982f9693db5817 100644
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diff --git a/docs/_build/html/py-modindex.html b/docs/_build/html/py-modindex.html
index 782ba59..2b5427b 100644
--- a/docs/_build/html/py-modindex.html
+++ b/docs/_build/html/py-modindex.html
@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Python Module Index — SSBtoolkit v1 documentation</title>
+ <title>Python Module Index — SSBtoolkit v1.0.1 documentation</title>
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
<!--[if lt IE 9]>
@@ -77,17 +77,35 @@
<h1>Python Module Index</h1>
<div class="modindex-jumpbox">
- <a href="#cap-s"><strong>s</strong></a>
+ <a href="#cap-b"><strong>b</strong></a> |
+ <a href="#cap-s"><strong>s</strong></a> |
+ <a href="#cap-u"><strong>u</strong></a>
</div>
<table class="indextable modindextable">
<tr class="pcap"><td></td><td> </td><td></td></tr>
+ <tr class="cap" id="cap-b"><td></td><td>
+ <strong>b</strong></td><td></td></tr>
+ <tr>
+ <td></td>
+ <td>
+ <a href="apidocs.html#module-Binding"><code class="xref">Binding</code></a></td><td>
+ <em></em></td></tr>
+ <tr class="pcap"><td></td><td> </td><td></td></tr>
<tr class="cap" id="cap-s"><td></td><td>
<strong>s</strong></td><td></td></tr>
<tr>
<td></td>
<td>
- <a href="apidocs.html#module-ssbtoolkit"><code class="xref">ssbtoolkit</code></a></td><td>
+ <a href="apidocs.html#module-Simulation"><code class="xref">Simulation</code></a></td><td>
+ <em></em></td></tr>
+ <tr class="pcap"><td></td><td> </td><td></td></tr>
+ <tr class="cap" id="cap-u"><td></td><td>
+ <strong>u</strong></td><td></td></tr>
+ <tr>
+ <td></td>
+ <td>
+ <a href="apidocs.html#module-Utils"><code class="xref">Utils</code></a></td><td>
<em></em></td></tr>
</table>
diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html
index 311d523..29084b6 100644
--- a/docs/_build/html/search.html
+++ b/docs/_build/html/search.html
@@ -3,7 +3,7 @@
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
- <title>Search — SSBtoolkit v1 documentation</title>
+ <title>Search — SSBtoolkit v1.0.1 documentation</title>
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<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
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index 95ef7fd..3276789 100644
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\ No newline at end of file
diff --git a/docs/apidocs.rst b/docs/apidocs.rst
index 388a749..9648907 100644
--- a/docs/apidocs.rst
+++ b/docs/apidocs.rst
@@ -1,7 +1,10 @@
API Documentation
=================
-.. automodule:: ssbtoolkit
+.. automodule:: Binding
:members:
-
+
+.. automodule:: Simulation
+ :members:
+
.. automodule:: Utils
:members:
diff --git a/docs/conf.py b/docs/conf.py
index 8571d44..d51c86f 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -12,7 +12,8 @@
#
import os
import sys
-sys.path.insert(0, os.path.abspath('../src/lib'))
+sys.path.insert(0, os.path.abspath('../ssbtoolkit'))
+sys.path.insert(0, os.path.abspath('../'))
# -- Project information -----------------------------------------------------
@@ -22,7 +23,7 @@ copyright = '2022, Rui Pedro Ribeiro'
author = 'Rui Pedro Ribeiro'
# The full version, including alpha/beta/rc tags
-release = 'v1'
+release = 'v1.0.1'
# -- General configuration ---------------------------------------------------
diff --git a/ssbtoolkit/Binding.py b/ssbtoolkit/Binding.py
new file mode 100644
index 0000000..e68ef4f
--- /dev/null
+++ b/ssbtoolkit/Binding.py
@@ -0,0 +1,126 @@
+#LIBRARIES
+import numpy as np
+import ssbtoolkit.Utils as utils
+
+"""Module to simulate ligand-target binding curves."""
+def __init__(self):
+ self.receptor_conc = None
+ self.lig_conc_range = None
+ self.pKd = None
+ self.submax_concentration = None
+
+def Bind(self, **kwargs):
+ """
+ Applies an function to calculate the fraction of occupited receptors at equilibrium.
+
+ :parameter receptor_conc: Required (kwarg flt): concentration of receptor
+ :parameter lig_conc_range: Required (kwarg array): array of range of ligand concentration
+ :parameter pKd: Required (kwarg flt): pKd value of the ligand
+ """
+
+ if 'receptor_conc' not in kwargs: raise TypeError("ERROR: receptor_conc is missing")
+ if 'lig_conc_range' not in kwargs: raise TypeError("ERROR: lig_conc_range is missing")
+ if 'pKd' not in kwargs: raise TypeError("ERROR: pKd is missing")
+
+ self._receptor_conc = kwargs.pop('receptor_conc')
+ self._lig_conc_range = kwargs.pop('lig_conc_range')
+ self._pKd = kwargs.pop('pKd')
+
+ binding_data=[]
+ for conc in self._lig_conc_range:
+ binding_data.append(utils.CalcOccupancy(self._receptor_conc, conc, 0, self._pKd, 0))
+ self.binding_data=binding_data
+ return self.binding_data
+
+def SubMaxConcentration(self):
+ """
+ Calculates the maximum bending point of a sigmoid-shaped curve according to the mathod of Sebaugh et al., 2003.
+
+ :parameter drug_receptor: Required (int): concentration of the receptor
+ :parameter lig_conc_range: Required (array): array of a range of ligand concentration
+ :return: instance .submax_concentration (flt)
+
+ .. note:: The minimization uses the Nelder-Mead method.
+ """
+
+ from scipy.optimize import curve_fit, minimize
+
+
+ def sigmoid(X, Bottom, Top, Kd, p):
+ return Bottom + (Top-Bottom)/(1+np.power((Kd/X),p))
+
+ xfit = np.geomspace(np.min(self._lig_conc_range), np.max(self._lig_conc_range), 50000) #warning: shoud this be the minimum and maximum of concentration
+ popt, pcov = curve_fit(sigmoid, self._lig_conc_range, self.binding_data, bounds=([np.min(self.binding_data),-np.inf,-np.inf, 0.5],[np.inf,np.max(self.binding_data),np.inf, 2.5]))
+
+
+ def sigmoid_deriv_b(x, a,d,c,b):
+ return (x/c)**b*(a - d)*np.log(x/c)/((x/c)**b + 1)**2
+
+ min_value = minimize(sigmoid_deriv_b, np.max(xfit), args=(popt[0],popt[1],popt[2],popt[3]), method = 'Nelder-Mead')
+
+ self.submax_concentration = round(min_value.x[0],3)
+ return self.submax_concentration
+
+def ShowCurve(self):
+ """
+ Plots ligand-target binding curve
+ """
+
+ #import plotly
+ import plotly.graph_objs as go
+ from scipy.optimize import curve_fit
+ from sklearn.preprocessing import minmax_scale
+
+ ##Fitting curve to the data
+
+ yy = minmax_scale(self.binding_data)*100
+
+ def equation_dose(X, Bottom, Top, EC50, p):
+ return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
+
+ popt, pcov = curve_fit(equation_dose, self._lig_conc_range, yy, bounds=([np.min(yy),-np.inf,-np.inf, 0.5],[np.inf,np.max(yy),np.inf, 2.5]))
+
+ xfit = np.geomspace(np.min(self._lig_conc_range), np.max(self._lig_conc_range), 50000) # These values are the same as the values for the simulation time and not ligand concentration
+ yfit = minmax_scale(equation_dose(xfit, *popt))*100
+
+
+
+ trace1 = go.Line(x=xfit, y=yfit, showlegend=False, name='radioligand')
+ if self.submax_concentration:
+ xsubmaximal = np.array(self.submax_concentration)
+ ysubmaximal = np.array(equation_dose(xsubmaximal, *popt))
+ trace2 = go.Scatter(x=xsubmaximal, y=ysubmaximal, showlegend=True, mode='markers', name='submaximal ({} μM)'.format(xsubmaximal),
+ marker=dict(size=14))
+ else: trace2=[]
+
+
+ layout = dict(title = '',
+ xaxis = dict(
+ title = '[ligand] μM',
+ type ='log',
+ exponentformat='e',
+ titlefont=dict(
+ size=20
+ ),
+ tickfont=dict(
+ size=20
+ )),
+ yaxis = dict(
+ title = '% occupied receptors',
+
+ titlefont=dict(
+ size=20),
+ tickfont=dict(
+ size=20)
+
+ ),
+ legend=dict(font=dict(size=15)),
+ autosize=False,
+ width=850,
+ height=650,
+ )
+ if self.submax_concentration:
+ fig = go.Figure(data=[trace1, trace2], layout=layout)
+ else:
+ fig = go.Figure(data=[trace1], layout=layout)
+ return fig
diff --git a/ssbtoolkit/Simulation.py b/ssbtoolkit/Simulation.py
new file mode 100644
index 0000000..263ff9a
--- /dev/null
+++ b/ssbtoolkit/Simulation.py
@@ -0,0 +1,1277 @@
+#LIBRARIES
+
+import sys, os, warnings, platform, site, importlib
+import pylab as pl
+from pysb.simulator import ScipyOdeSimulator
+from scipy.optimize import curve_fit
+from sklearn.preprocessing import minmax_scale
+import plotly.graph_objs as go
+import pandas as pd
+import numpy as np
+import ssbtoolkit.Utils as utils
+
+sys.path.insert(0, os.path.abspath(os.path.split(os.path.realpath(__file__))[0]))
+warnings.simplefilter(action='ignore')
+
+distpath = site.getsitepackages()[0]
+if platform.system() == 'Linux':
+ BioNetGen=os.path.join(distpath, 'bionetgen/bng-linux:')
+elif platform.system() == 'Darwin':
+ BioNetGen=os.path.join(distpath, 'bionetgen/bng-mac:')
+elif platform.system()=='Windows':
+ BioNetGen=os.path.join(distpath, 'bionetgen/bng-win:')
+else:
+ raise ValueError('BioNetGen error. Platform unknown! The pygomodo was tested in Linux and Darwin (Mac) platforms.')
+os.environ['PATH']=BioNetGen+os.environ['PATH']
+
+pathways_path=(os.path.join(os.path.split(os.path.realpath(__file__))[0], 'pathways'))
+
+
+"""
+Module to simulate the mathematical models of the signaling pathways.
+"""
+class Activation:
+ """
+ Simulation of the activation of signaling pathways (i.e. activation by agonists)
+ """
+ def __init__(self):
+ self._ligands=None
+ self._affinities=None
+ self._pathway=None
+ self._receptor_conc=None
+ self._lig_conc_range=None
+ self._ttotal=None
+ self._nsteps=None
+ self._binding_kinetics=True
+ self._binding_kinetic_parameters=None
+ self.simulation_data=None
+ self.processed_data=None
+
+ def SetSimulationParameters(self, **kwargs):
+ """
+ :parameter ligands: Required (kwargs list): list of ligands' names (str)
+ :parameter affinities: Required (kwargs list): list of pKd values (flt)
+ :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
+ :parameter receptor_conc: Required (kwargs flt): receptors concentration (nM)
+ :parameter lig_conc_range: Required (kwargs array): range of ligands' concentration
+ :parameter ttotal: Required (kwargs int): simulation time (seconds)
+ :parameter nsteps: Required (kwargs int): simulation time step
+ :parameter binding_kinetics: Optional (kwargs boolean): default (False)
+
+
+ .. warning:: the order of the lists of ligands names and affinities list must be the same.
+
+ """
+ self._ligands= kwargs.pop('ligands')
+ if 'affinities' in kwargs:
+ self._affinities=kwargs.pop('affinities')
+ self._pathway=kwargs.pop('pathway')
+ self._receptor_conc=kwargs.pop('receptor_conc')
+ self._lig_conc_range=kwargs.pop('lig_conc_range')
+ self._ttotal=kwargs.pop('ttotal')
+ self._nsteps=kwargs.pop('nsteps')
+ self._binding_kinetics=kwargs.pop('binding_kinetics')
+ if 'binding_kinetic_parameters' in kwargs:self._binding_kinetic_parameters=kwargs.pop('binding_kinetic_parameters')
+ self._DefaultPathwayParametersDataFrame=pd.DataFrame()
+
+ return
+
+ def PathwayParameters(self):
+ """
+ Display table with default pathway parameters.
+
+ .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
+ """
+ import qgrid
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+
+ col_opts = { 'editable': False, 'sortable':False}
+ col_defs = {'Value': { 'editable': True, 'width': 150 }}
+ self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
+ return self._DefaultPathwayParametersTable
+
+ def UserPathwayParameters(self, path):
+ """
+ Import user pathway parameters.
+
+ :parameter path: Required (kwarg str): directory path
+ """
+ import qgrid
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
+ col_opts = { 'editable': False, 'sortable':False}
+ col_defs = {'Value': { 'editable': True, 'width': 150 }}
+ self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
+ return self._DefaultPathwayParametersTable
+
+ def PathwayParametersToCSV(self, path):
+ """
+ Export pathway parameters into CSV format.
+
+ :parameter path: Required (kwarg str): directory path
+ """
+ self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
+ print('saved in:', path)
+ return
+
+ def Reactions(self):
+ """
+ Display pathway reactions.
+ """
+ from IPython.display import display, HTML
+ display(HTML("<style>.container {width:90% !important}</style>"))
+ return pd.read_csv(pathways_path+'/{}_reactions.csv'.format(self._pathway))
+
+ def Run(self):
+ '''
+ This function runs the pathway simulation and returns the raw simulation data.
+ '''
+
+ #Check inputs
+ if self._ligands==None: raise TypeError("ligands list undefined.")
+ elif self._pathway==None: raise TypeError("pathway name undefined.")
+ elif self._binding_kinetics==False and self._affinities==None: raise TypeError("affinity_values_dict undefined.")
+ elif self._binding_kinetics==True and self._affinities==None: pass
+ elif self._lig_conc_range.any() == False: raise TypeError("lig_conc_range undefined.")
+ elif self._ttotal==None: raise TypeError("ttotal undefined.")
+ elif self._nsteps==None: raise TypeError("nsteps undefined.")
+ elif self._receptor_conc==None: raise TypeError("receptor_conc undefined.")
+ else: pass
+
+
+ #Check Pathway availability and import it
+ available_pathways = ['Gs', 'Gi', 'Gq']
+ if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
+ if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Pathways available: "Gs", "Gi", "Gq".')
+ mypathway = importlib.import_module('.'+self._pathway, package='ssbtoolkit.pathways')
+
+ #Get default pathway parameters
+ if self._DefaultPathwayParametersDataFrame.empty and self._binding_kinetic_parameters==None:
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+ self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
+
+ elif self._DefaultPathwayParametersDataFrame.empty is False and self._binding_kinetic_parameters is None:
+ try:
+ #extract data from qgrid
+ newparameters = self._DefaultPathwayParametersTable.get_changed_df()
+ self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
+ except:
+ self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
+
+ elif self._DefaultPathwayParametersDataFrame.empty and self._binding_kinetic_parameters is not None:
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+
+ elif self._DefaultPathwayParametersDataFrame.empty is False and self._binding_kinetic_parameters is not None:
+ try:
+ #extract data from qgrid
+ newparameters = self._DefaultPathwayParametersTable.get_changed_df()
+ self._PathwayParameters = {**newparameters.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters}
+ except:
+ self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters}
+
+ #Input
+ t = pl.geomspace(0.00001, self._ttotal, num=self._nsteps) # (a,b,c); a is the starting time ; b is the total time simulated ; c is the number of points
+
+
+ #Output
+ simulation_data={}
+
+ #Function
+ for ligand in self._ligands:
+ ligand_name = os.path.splitext(str(ligand))[0]
+ data=[]
+ utils.PrintProgressBar(0, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
+
+ ###DANGER ZONE###
+ if self._binding_kinetic_parameters is not None:
+ self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters[self._ligands.index(ligand)]}
+ ######################
+
+ for idx in range(len(self._lig_conc_range)):
+
+ ligand_conc = self._lig_conc_range[idx]
+ if self._binding_kinetics == False:
+ #get LR conc
+ parameters = {**self._PathwayParameters, 'R_init':self._receptor_conc}
+ LR_conc_init = utils.CalcOccupancy(self._receptor_conc, ligand_conc, 0, self._affinities[self._ligands.index(ligand)], 0)
+ mymodel = mypathway.network(LR=LR_conc_init, kinetics=False, **parameters)
+ simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
+ yout = simres.all
+
+ elif self._binding_kinetics == True:
+ parameters={**self._PathwayParameters,'R_init':self._receptor_conc, 'L_init':self._lig_conc_range[idx] }
+ mymodel = mypathway.network(kinetics=True, **parameters)
+ simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
+ yout = simres.all
+
+ d1={'ligand_conc':ligand_conc, 'time':t }
+
+ for idx2 in range(len(mypathway.list_of_observables)):
+ d2={mypathway.list_of_observables[idx2]:yout[mypathway.list_of_observables[idx2]]}
+ d1.update(d2)
+ data.append(d1)
+ utils.PrintProgressBar(idx + 1, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
+
+
+ simulation_data[ligand_name] = {'sim_data':data,
+ 'label':ligand_name,}
+ self.simulation_data = simulation_data
+ return
+
+ def Analysis(self):
+ '''
+ This function calculates the dose-response effect.
+
+ :return: instance of processed_data
+ '''
+
+ if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.activation.run() must be run first.')
+
+ # Define all the lists and dictionaries used in this function
+ raw_data=[]
+ normalized_data=[]
+ dose={}
+
+ #defining concentration range
+ lig_conc_min = self._lig_conc_range.min()
+ lig_conc_max = self._lig_conc_range.max()
+
+ #Main function
+ for ligand in self.simulation_data:
+
+ #definig and dictionaries used in this loop:
+ raw_data_dict={}
+ normalized_data_dict={}
+
+ # Calculate dose-response curve
+ #get metabolite concentration, rescale, and transform data if pathway/metabolite decrease
+ # metabolite_raw is not normalized
+ if self._pathway == 'Gi' or self._pathway == 'Gz(Gi)':
+ metabolite='cAMP'
+ metabolite_conc_raw=[]
+ for i in range(len(self._lig_conc_range)):
+ n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
+ metabolite_conc_raw.append(n)
+ metabolite_conc_norm = minmax_scale(1-np.array(metabolite_conc_raw))
+ elif self._pathway == 'Gs':
+ metabolite='cAMP'
+ metabolite_conc_raw=[]
+ for i in range(len(self._lig_conc_range)):
+ n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
+ metabolite_conc_raw.append(n)
+ metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
+ elif self._pathway == 'Gq':
+ metabolite='IP3'
+ metabolite_conc_raw=[]
+ for i in range(len(self._lig_conc_range)):
+ n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
+ metabolite_conc_raw.append(n)
+ metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
+ else: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
+
+
+ ## save results
+ raw_data_dict['x']=self._lig_conc_range
+ raw_data_dict['y']=metabolite_conc_raw
+ raw_data_dict['label']=self.simulation_data[ligand]['label']
+
+ normalized_data_dict['x']=self._lig_conc_range
+ normalized_data_dict['y']=metabolite_conc_norm
+ normalized_data_dict['label']=self.simulation_data[ligand]['label']
+
+ ## create a list of all data
+ raw_data.append(raw_data_dict)
+ normalized_data.append(normalized_data_dict)
+
+ ##Fitting curve to the data
+
+ def equation_dose(X, Bottom, Top, EC50, p):
+ return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
+
+ popt_EC50, pcov = curve_fit(equation_dose, self._lig_conc_range, metabolite_conc_norm, bounds=([np.min(metabolite_conc_norm),-np.inf,-np.inf, 0.5],[np.inf,np.max(metabolite_conc_norm),np.inf, 2.5]))
+
+ xfit_EC50 = np.geomspace(lig_conc_min, lig_conc_max, 50000) # These values are the same as the values for the simulation time and not ligand concentration
+ yfit_EC50 = equation_dose(xfit_EC50, *popt_EC50)
+
+ fit_EC50={'x':xfit_EC50, 'y':yfit_EC50, 'label':self.simulation_data[ligand]['label']}
+
+ dose[ligand] = {'raw_data': raw_data_dict,
+ 'normalized_data':normalized_data_dict ,
+ 'fitted_data': fit_EC50,
+ 'EC50 (μM)': round(popt_EC50[2],5),
+ 'pEC50': round(-np.log10(popt_EC50[2]*1E-6),2)}
+
+ self.processed_data=dose
+ return
+
+ def ShowCurve(self, save=False, filename=None):
+ '''
+ Plots the dose-response curve.
+ '''
+
+ if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.activation.run() must be run first.')
+
+ import plotly
+ import plotly.graph_objs as go
+ import plotly.offline as pyoff
+
+ colors = plotly.colors.DEFAULT_PLOTLY_COLORS
+
+ plot_data=[]
+
+ color_id=0
+ for ligand in self.processed_data:
+ trace_norm = go.Scatter(x=self.processed_data[ligand]['normalized_data']['x'],
+ y=minmax_scale(self.processed_data[ligand]['normalized_data']['y'])*100 ,
+ mode='markers',
+ showlegend=True,
+ name=self.processed_data[ligand]['normalized_data']['label'],
+ marker=dict(color=colors[color_id]))
+ plot_data.append(trace_norm)
+
+ trace_fitted = go.Scatter(x=self.processed_data[ligand]['fitted_data']['x'],
+ y=minmax_scale(self.processed_data[ligand]['fitted_data']['y'])*100,
+ mode='lines',
+ showlegend=False,
+ name=self.processed_data[ligand]['fitted_data']['label'],
+ line=dict(color=colors[color_id]))
+ plot_data.append(trace_fitted)
+ color_id +=1
+
+ layout = dict(title = '',
+ xaxis = dict(
+ title = '[ligand] μM',
+ type ='log',
+ #range = [-3, 2],
+ exponentformat='e',
+ titlefont=dict(
+ size=20
+ ),
+ tickfont=dict(
+ size=20
+ )),
+ yaxis = dict(
+ title = '% Response',
+ #range = [0, 100],
+ titlefont=dict(
+ size=20),
+ tickfont=dict(
+ size=20)
+
+ ),
+ legend=dict(font=dict(size=15)),
+ autosize=False,
+ width=850,
+ height=650,
+ )
+
+ fig = go.Figure(data=plot_data, layout=layout)
+ #fig['layout']['yaxis'].update(autorange = True)
+
+ if save==True:
+ if filename==None:
+ filename='plot.html'
+ return pyoff.plot(fig, filename=filename)
+ else:
+ ext = os.path.splitext(filename)[-1]
+ if ext == '.png': fig.write_image(filename, scale=3)
+ elif ext == '.html': pyoff.plot(fig, filename=filename)
+ else: raise TypeError("extension not valid. Use png or html.")
+ elif save ==False: return fig
+ return
+
+ def ShowPotency(self):
+ '''
+ Return the potency values as a pandas DataFrame.
+ '''
+ import pandas as pd
+ data = Simulation.Activation.PotencyToDict(self)
+ df = pd.DataFrame.from_dict(data, orient='index')
+ return df
+
+ def PotencyToDict(self):
+ '''
+ Convert potencies into a dictionary.
+ '''
+
+ #dependencies
+ if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.activation.analysis() must be run first.')
+
+ kvalues={}
+ for ligand in self.processed_data:
+ IC50 = list(self.processed_data[ligand].keys())[-2]
+ IC50_value = self.processed_data[ligand][IC50]
+ pIC50 = list(self.processed_data[ligand].keys())[-1]
+ pIC50_value = self.processed_data[ligand][pIC50]
+ kvalues[ligand]={IC50:IC50_value, pIC50:pIC50_value}
+ return kvalues
+
+ def PotencyToCSV(self, path):
+ '''
+ Exports the potency values into csv format.
+
+ :parameter path: Required (kwarg str): directory path to save the csv file
+ '''
+
+ data = Simulation.Activation.PotencyToDict(self)
+ df = pd.DataFrame.from_dict(data, orient='index')
+ df.to_csv(path, index=False)
+ return
+
+class Inhibition:
+ """
+ Simulation of the inhibition of signaling pathways (i.e. inhibition by antagonists).
+ """
+ def __init__(self):
+ self._agonist=None
+ self._agonist_affinity=None
+ self._agonist_submaximal_conc=None
+ self._antagonists=None
+ self._antagonists_affinities=None
+ self._pathway=None
+ self._receptor_conc=None
+ self._lig_conc_range=None
+ self._ttotal=None
+ self._nsteps=None
+ self._binding_kinetics=False
+ self._binding_kinetic_parameters=None
+ self.simulation_data=None
+ self.processed_data=None
+
+ def SetSimulationParameters(self, **kwargs):
+ """
+ :parameter agonist: Required (kwargs str): agonist name
+ :parameter agonist_affinity: Required (kwargs flt): agonist pKd value
+ :parameter agonist_submaximal_conc: Required (kwargs flt): agonist submaximal concentration
+ :parameter antagonists: Required (kwargs list):list of antagonists names (str)
+ :parameter antagonists_affinities: Required (kwargs list): list of antagonists affinity values (flt)
+ :parameter antagonists_conc_range: Required (kwargs array): range of ligands' concentration (nM)
+ :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
+ :parameter receptor_conc: Required (kwargs flt): receptors concentration (nM)
+ :parameter ttotal: Required (kwargs int): simulation time (seconds)
+ :parameter nsteps: Required (kwargs int): simulation time step
+ :parameter kinetics: Optional (kwargs boolean): default (False)
+
+
+ :return: instances of all parameters
+
+ .. warning:: the order of the lists of the antagonists names and affinities list must be the same.
+
+ """
+ self._agonist= kwargs.pop('agonist')
+ self._agonist_affinity=kwargs.pop('agonist_affinity')
+ self._agonist_submaximal_conc=kwargs.pop('agonist_submaximal_conc')
+ self._antagonists=kwargs.pop('antagonists')
+ self._antagonists_affinities=kwargs.pop('antagonists_affinities')
+ self._pathway=kwargs.pop('pathway')
+ self._receptor_conc=kwargs.pop('receptor_conc')
+ self._lig_conc_range=kwargs.pop('lig_conc_range')
+ self._ttotal=kwargs.pop('ttotal')
+ self._nsteps=kwargs.pop('nsteps')
+ if 'kinetics' in kwargs:
+ self._binding_kinetics=kwargs.pop('kinetics')
+ if self._binding_kinetics==True: raise TypeError("The of Kinetic parameters during an inhibition simulation it is not supported yet.")
+ else: self._binding_kinetics=False
+ if 'binding_kinetic_parameters' in kwargs:
+ self._binding_kinetic_parameters=kwargs.pop('binding_kinetic_parameters')
+ self._DefaultPathwayParametersDataFrame=pd.DataFrame()
+
+ return
+
+ def PathwayParameters(self):
+ """
+ Display table with default pathway parameters.
+
+ .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
+ """
+ import qgrid
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+
+ col_opts = { 'editable': False, 'sortable':False}
+ col_defs = {'Value': { 'editable': True, 'width': 150 }}
+ self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
+ return self._DefaultPathwayParametersTable
+
+ def UserPathwayParameters(self, path):
+ """
+ Import user pathway parameters.
+
+ :parameter path: Required (kwarg str): directory path
+ """
+ import qgrid
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
+ col_opts = { 'editable': False, 'sortable':False}
+ col_defs = {'Value': { 'editable': True, 'width': 150 }}
+ self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
+ return self._DefaultPathwayParametersTable
+
+ def PathwayParametersToCSV(self, path):
+ """
+ Export pathway parameters into CSV format.
+
+ :parameter path: Required (kwarg str): directory path
+ """
+ self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
+ print('saved in:', path)
+ return
+
+ def Reactions(self):
+ """
+ Display pathway reactions.
+ """
+ from IPython.display import display, HTML
+ display(HTML("<style>.container {width:90% !important}</style>"))
+ return pd.read_csv(pathways_path+'/{}_reactions.csv'.format(self._pathway))
+
+ def Run(self):
+ '''
+ This function runs the pathway simulation and returns the raw simulation data.
+ '''
+
+ #Check inputs
+ if self._agonist==None: raise TypeError("agonist undefined.")
+ elif self._agonist_affinity==None: raise TypeError("agonist_affinity undifined.")
+ elif self._antagonists==None: raise TypeError("antagonists list undefined.")
+ elif self._antagonists_affinities==None: raise TypeError("antagonists affinity values undefined.")
+ elif self._pathway==None: raise TypeError("pathway undefined.")
+ elif self._lig_conc_range.any() == False: raise TypeError("lig_conc_range undefined.")
+ elif self._agonist_submaximal_conc == None: raise TypeError("agonist_submaximal_conc undifined.")
+ elif self._ttotal==None: raise TypeError("ttotal undefined.")
+ elif self._nsteps==None: raise TypeError("nsteps undefined.")
+ elif self._receptor_conc==None: raise TypeError("receptor_conc undefined.")
+ elif self._binding_kinetics==True: raise TypeError("The of Kinetic parameters during an inhibition simulation it is not supported yet.")
+ else: pass
+
+ #check pathway
+ available_pathways = ['Gs', 'Gi', 'Gq']
+ if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
+ if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
+ mypathway = importlib.import_module('.'+self._pathway, package='ssbtoolkit.pathways')
+
+ #Get default pathway parameters
+ if self._DefaultPathwayParametersDataFrame.empty:
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+ self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
+
+ elif self._DefaultPathwayParametersDataFrame.empty is False:
+ try:
+ #extract data from qgrid
+ newparameters = self._DefaultPathwayParametersTable.get_changed_df()
+ self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
+ except:
+ self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
+
+ #Input
+ t = pl.geomspace(0.00001, self._ttotal, num=self._nsteps) # (a,b,c); a is the starting time ; b is the total time simulated ; c is the number of points
+
+ #Output
+ simulation_data={}
+
+ #Function
+ for ligand in self._antagonists:
+ ligand_name = os.path.splitext(ligand)[0]
+ data=[]
+ utils.PrintProgressBar(0, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
+
+ for idx in range(len(self._lig_conc_range)):
+
+ ligand_conc = self._lig_conc_range[idx]
+
+ #get LR conc
+ parameters = {**self._PathwayParameters, 'R_init':self._receptor_conc}
+ LR_conc_init = utils.CalcOccupancy(self._receptor_conc, self._agonist_submaximal_conc, ligand_conc, self._agonist_affinity, self._antagonists_affinities[self._antagonists.index(ligand)])
+ mymodel = mypathway.network(LR=LR_conc_init, kinetics=False, **parameters)
+ simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
+ yout = simres.all
+
+ d1={'ligand_conc':ligand_conc, 'time':t }
+
+ for idx2 in range(len(mypathway.list_of_observables)):
+ d2={mypathway.list_of_observables[idx2]:yout[mypathway.list_of_observables[idx2]]}
+ d1.update(d2)
+ data.append(d1)
+ utils.PrintProgressBar(idx + 1, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
+
+ simulation_data[ligand_name] = {'sim_data':data,
+ 'label':self._agonist+' + ' + ligand_name}
+
+ self.simulation_data=simulation_data
+ return
+
+ def Analysis(self):
+ '''
+ This function calculates the dose-response effect.
+
+ :return: instance processed_data
+ '''
+ #dependencies
+ if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.inhibition.run() must be run first.')
+
+ # Define all the lists and dictionaries used in this function
+ raw_data=[]
+ normalized_data=[]
+ dose={}
+
+ #defining concentration range
+ #defining concentration range
+ lig_conc_min = self._lig_conc_range.min()
+ lig_conc_max = self._lig_conc_range.max()
+
+ #Main function
+ for ligand in self.simulation_data:
+
+ #definig and dictionaries used in this loop:
+ raw_data_dict={}
+ normalized_data_dict={}
+
+ # Calculate dose-response curve
+ #get metabolite concentration, rescale, and transform data if pathway/metabolite decrease
+ # metabolite_raw is not normalized
+ if self._pathway == 'Gi' or self._pathway == 'Gz(Gi)':
+ metabolite='cAMP'
+ metabolite_conc_raw=[]
+ for i in range(len(self._lig_conc_range)):
+ n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
+ metabolite_conc_raw.append(n)
+ metabolite_conc_norm = minmax_scale(1-np.array(metabolite_conc_raw))
+ elif self._pathway == 'Gs':
+ metabolite='cAMP'
+ metabolite_conc_raw=[]
+ for i in range(len(self._lig_conc_range)):
+ n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
+ metabolite_conc_raw.append(n)
+ metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
+ elif self._pathway == 'Gq':
+ metabolite='IP3'
+ metabolite_conc_raw=[]
+ for i in range(len(self._lig_conc_range)):
+ n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
+ metabolite_conc_raw.append(n)
+ metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
+ else: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
+
+
+ ## save results
+ raw_data_dict['x']=self._lig_conc_range
+ raw_data_dict['y']=metabolite_conc_raw
+ raw_data_dict['label']=self.simulation_data[ligand]['label']
+
+ normalized_data_dict['x']=self._lig_conc_range
+ normalized_data_dict['y']=metabolite_conc_norm
+ normalized_data_dict['label']=self.simulation_data[ligand]['label']
+
+ ## create a list of all data
+ raw_data.append(raw_data_dict)
+ normalized_data.append(normalized_data_dict)
+
+ ##Fitting curve to the data
+
+ def equation_dose(X, Bottom, Top, EC50, p):
+ return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
+
+ popt_IC50, pcov = curve_fit(equation_dose, self._lig_conc_range, metabolite_conc_norm, bounds=([np.min(metabolite_conc_norm),-np.inf,-np.inf, 0.5],[np.inf,np.max(metabolite_conc_norm),np.inf, 2.5]))
+
+ xfit_IC50 = np.geomspace(lig_conc_min, lig_conc_max, 50000) # These values are the same as the values for the simulation time and not ligand concentration
+ yfit_IC50 = equation_dose(xfit_IC50, *popt_IC50)
+
+ fit_IC50={'x':xfit_IC50, 'y':yfit_IC50, 'label':self.simulation_data[ligand]['label']}
+
+ dose[ligand] = {'raw_data': raw_data_dict,
+ 'normalized_data':normalized_data_dict ,
+ 'fitted_data': fit_IC50,
+ 'IC50 (μM)': round(popt_IC50[2],5),
+ 'pIC50': round(-np.log10(popt_IC50[2]*1E-6),2)}
+
+ self.processed_data=dose
+ return
+
+ def ShowCurve(self, save=False, filename=None):
+ '''
+ Plot the dose-response curve.
+ '''
+ #dependencies
+ if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.inhibition.analysis() must be run first.')
+
+ import plotly
+ import plotly.graph_objs as go
+ import plotly.offline as pyoff
+
+ colors = plotly.colors.DEFAULT_PLOTLY_COLORS
+
+ plot_data=[]
+
+ color_id=0
+ for ligand in self.processed_data:
+ trace_norm = go.Scatter(x=self.processed_data[ligand]['normalized_data']['x'],
+ y=minmax_scale(self.processed_data[ligand]['normalized_data']['y'])*100 ,
+ mode='markers',
+ showlegend=True,
+ name=self.processed_data[ligand]['normalized_data']['label'],
+ marker=dict(color=colors[color_id]))
+ plot_data.append(trace_norm)
+
+ trace_fitted = go.Scatter(x=self.processed_data[ligand]['fitted_data']['x'],
+ y=minmax_scale(self.processed_data[ligand]['fitted_data']['y'])*100,
+ mode='lines',
+ showlegend=False,
+ name=self.processed_data[ligand]['fitted_data']['label'],
+ line=dict(color=colors[color_id]))
+ plot_data.append(trace_fitted)
+ color_id +=1
+
+ layout = dict(title = '',
+ xaxis = dict(
+ title = '[ligand] μM',
+ type ='log',
+ #range = [-4, 2],
+ exponentformat='e',
+ titlefont=dict(
+ size=20
+ ),
+ tickfont=dict(
+ size=20
+ )),
+ yaxis = dict(
+ title = '% Response',
+ #range = [0, 100],
+ titlefont=dict(
+ size=20),
+ tickfont=dict(
+ size=20)
+
+ ),
+ legend=dict(font=dict(size=15)),
+ autosize=False,
+ width=850,
+ height=650,
+ )
+
+ fig = go.Figure(data=plot_data, layout=layout)
+ if save==True:
+ if filename==None:
+ filename='plot.html'
+ return pyoff.plot(fig, filename=filename)
+ else:
+ ext = os.path.splitext(filename)[-1]
+ if ext == '.png': fig.write_image(filename, scale=3)
+ elif ext == '.html': pyoff.plot(fig, filename=filename)
+ else: raise TypeError("extension not valid. Use png or html.")
+ elif save ==False: return fig
+ return
+
+ def ShowPotency(self):
+ '''
+ Return the potency values as a pandas DataFrame.
+ '''
+ import pandas as pd
+ data = Simulation.Inhibition.PotencyToDict(self)
+ df = pd.DataFrame.from_dict(data, orient='index')
+ return df
+
+ def PotencyToDict(self):
+ '''
+ Convert potencies into a dictionary.
+ '''
+ #dependencies
+ if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.inhibition.analysis() must be run first.')
+
+ kvalues={}
+ for ligand in self.processed_data:
+ IC50 = list(self.processed_data[ligand].keys())[-2]
+ IC50_value = self.processed_data[ligand][IC50]
+ pIC50 = list(self.processed_data[ligand].keys())[-1]
+ pIC50_value = self.processed_data[ligand][pIC50]
+ kvalues[ligand]={IC50:IC50_value, pIC50:pIC50_value}
+ return kvalues
+
+ def PotencyToCSV(self, path):
+ '''
+ Exports the potency values into csv format.
+
+ :parameter path: Required (kwarg str): directory path to save the csv file
+ '''
+ data = Simulation.Inhibition.PotencyToDict(self)
+ df = pd.DataFrame.from_dict(data, orient='index')
+ df.to_csv(path, index=False)
+ return
+
+class FitModel:
+ """
+ Fit a model to experimental data.
+
+ .. note:: This class was developed to reproduce data from a specific experimental setup. Please see tutorial 4 (OXTR pathay). Use carefully!
+ """
+ def __init__(self):
+
+ #fitting parameters
+ self._expratio = None
+ self._seed = None
+ self._maxiter = None
+ self._seed_incrementor = None
+ self._target_parameter = None
+
+ #Pathway parameters
+ self._ttotal = None
+ self._nsteps = None
+ self._pathway = None
+ self._observable = None
+ self.pathway_parameters = {}
+
+ def SetSimulationParameters(self, **kwargs):
+ """
+ :parameter pathway_parameters: Required (kwargs): dict of pathway parameters
+ :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
+ :parameter ttotal: Required (kwargs int): simulation time (seconds)
+ :parameter nsteps: Required (kwargs int): simulation time step
+ :parameter observable: Required (kwargs str): molecular specie to be measured
+
+ :return: instances of all parameters
+
+ """
+
+ if 'pathway_parameters' in kwargs:
+ self.pathway_parameters = kwargs.pop('pathway_parameters')
+ #print('pathway_parameters YES')
+ self._DefaultPathwayParametersDataFrame=pd.DataFrame()
+ if 'ttotal' in kwargs:
+ self._ttotal = int(kwargs.pop('ttotal'))
+ print('ttotal =', self._ttotal)
+ else: raise TypeError("ttotal undefined.")
+
+ if 'nsteps' in kwargs:
+ self._nsteps = int(kwargs.pop('nsteps', 1000))
+ print('nsteps =', self._nsteps)
+
+ if 'pathway' in kwargs:
+ self._pathway = str(kwargs.pop('pathway'))
+ print('pathway ->', self._pathway)
+ else: raise TypeError("pathway undefined.")
+
+ available_pathways = ['Gs', 'Gi', 'Gq', 'OXTR_pathway']
+ if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
+ if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
+
+
+ if'observable' in kwargs:
+ self._observable = str(kwargs.pop('observable'))
+ print('observable ->', self._observable)
+ else: raise TypeError("observable undefined.")
+
+ return
+
+ def PathwayParameters(self):
+ """
+ Display table with default pathway parameters.
+
+ .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
+ """
+ import qgrid
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+
+ col_opts = { 'editable': False, 'sortable':False}
+ col_defs = {'Value': { 'editable': True, 'width': 150 }}
+ self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
+ return self._DefaultPathwayParametersTable
+
+ def UserPathwayParameters(self, path):
+ """
+ Import user pathway parameters.
+
+ :parameter path: Required (kwarg str): directory path
+ """
+ import qgrid
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
+ col_opts = { 'editable': False, 'sortable':False}
+ col_defs = {'Value': { 'editable': True, 'width': 150 }}
+ self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
+ return self._DefaultPathwayParametersTable
+
+ def PathwayParametersToCSV(self, path):
+ """
+ Export pathway parameters into CSV format.
+
+ :parameter path: Required (kwarg str): directory path
+ """
+ self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
+ print('saved in:', path)
+ return
+
+ def Reactions(self):
+ """
+ Display pathway reactions.
+ """
+ from IPython.display import display, HTML
+ display(HTML("<style>.container {width:90% !important}</style>"))
+ return pd.read_csv(pathways_path+'/{}_reactions.csv'.format(self._pathway))
+
+ def Run(self, **kwargs):
+ """
+ Fits of the model to experimental data.
+
+ :parameter expratio: Required (kwargs flt): experimental signalling specie concentration ratio
+ :parameter target_parameter: Required (kwargs str):kinetic parameter to me modified
+ :parameter maxiter: Required (kwargs int): maximum number of iteration
+ :parameter seed: Required (kwargs flt): ramdom seed for scaling the modified parameter
+ :parameter seed_incrementor: Required (kwargs flt): seed incrementor (each iteration will increment the seed by this value)
+ :parameter seed_decrementor: Required (kwargs flt): seed decrementor (each iteration will decrement the seed by this value)
+
+ """
+
+ from scipy.signal import find_peaks
+ import decimal
+ #fitting parameters
+ if 'expratio' in kwargs:
+ self._expratio = float(kwargs.pop('expratio'))
+ print('expratio =', self._expratio)
+ else: raise TypeError("exratio undefined.")
+
+ if 'seed' in kwargs:
+ self._seed = float(kwargs.pop('seed'))
+ print('seed =', self._seed)
+ else: raise TypeError("seed undefined.")
+
+ if 'maxiter' in kwargs:
+ self._maxiter = int(kwargs.pop('maxiter', 100))
+ print('maxiter =', self._maxiter)
+
+ if 'seed_incrementor' in kwargs:
+ self._seed_incrementor = float(kwargs.pop('seed_incrementor', 0.1))
+ print('seed_incrementor =', self._seed_incrementor)
+
+ if 'seed_decrementor' in kwargs:
+ self._seed_decrementor = float(kwargs.pop('seed_decrementor', 0.1))
+ print('seed_decrementor =', self._seed_decrementor)
+
+ if 'target_parameter' in kwargs:
+ self._target_parameter = str(kwargs.pop('target_parameter'))
+ print('target_parameter ->', self._target_parameter)
+ else: raise TypeError("target_parameter undefined.")
+
+
+ #Get default pathway parameters
+ if self._DefaultPathwayParametersDataFrame.empty and self.pathway_parameters==None:
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+ self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
+
+ elif self._DefaultPathwayParametersDataFrame.empty is False and self.pathway_parameters is None:
+ try:
+ #extract data from qgrid
+ newparameters = self._DefaultPathwayParametersTable.get_changed_df()
+ self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
+ except:
+ self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
+
+ elif self._DefaultPathwayParametersDataFrame.empty and self.pathway_parameters is not None:
+ self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
+ self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
+
+ elif self._DefaultPathwayParametersDataFrame.empty is False and self.pathway_parameters is not None:
+ try:
+ #extract data from qgrid
+ newparameters = self._DefaultPathwayParametersTable.get_changed_df()
+ self._PathwayParameters = {**newparameters.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
+ except:
+ self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
+
+
+
+ #simulation parameters:
+ if not self._ttotal:
+ raise TypeError("simulation parameters unknown. Set the the simulation parameters first wiht set_simulation_parameters()")
+
+ #Main function
+ mypathway = importlib.import_module('.'+self._pathway, package='ssbtoolkit.pathways')
+ self.simtime = pl.geomspace(0.00001, self._ttotal, num=self._nsteps)
+
+ #Simulation 1
+ pathway_model = mypathway.network(LR=None, kinetics=True, **self._PathwayParameters)
+ sim1 = ScipyOdeSimulator(pathway_model, tspan=self.simtime,compiler='cython').run()
+ self.simres1 = sim1.all
+
+ def calc_ratio(self):
+
+
+ #Simulation 2
+ sim2 = ScipyOdeSimulator(mypathway.network(**self.new_pathway_parameters), tspan=self.simtime, compiler='cython').run()
+ self.simres2 = sim2.all
+
+ #analysis
+ obs_name = 'obs_'+self._observable
+ obs_1 = self.simres1[obs_name]
+ obs_2 = self.simres2[obs_name]
+
+ if 'time_in' in self.new_pathway_parameters:
+ self._time = np.take(self.simtime, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
+ obs_curve_1 = np.take(obs_1, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
+ obs_curve_2 = np.take(obs_2, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
+
+ else:
+ self._time = np.take(self.simtime, np.where(self.simtime>0))[0]
+ obs_curve_1 = np.take(obs_1, np.where(self.simtime > 0))[0]
+ obs_curve_2 = np.take(obs_2, np.where(self.simtime > 0))[0]
+
+ obs_peaks_1, _ = find_peaks(obs_curve_1)
+ obs_peaks_2, _ = find_peaks(obs_curve_2)
+
+ vmax_obs_curve_1 = obs_curve_1[obs_peaks_1][-1]*1E3
+ vmax_obs_curve_2 = obs_curve_2[obs_peaks_2][-1]*1E3
+
+ obs_ratio = round(vmax_obs_curve_2/vmax_obs_curve_1, abs(decimal.Decimal(str(self._expratio).rstrip('0')).as_tuple().exponent))
+
+ self._obs_curve_1=obs_curve_1
+ self._obs_curve_2=obs_curve_2
+ self._obs_peaks_1=obs_peaks_1
+ self._obs_peaks_2=obs_peaks_2
+ self._vmax_obs_curve_1=vmax_obs_curve_1
+ self._vmax_obs_curve_2=vmax_obs_curve_2
+
+ return obs_ratio
+
+ self._iteration=1
+ print('\n')
+
+ self._lst_ratio=[]
+ self._lst_seed=[]
+
+ for idx in range(self._maxiter):
+
+ prefix = 'iteration'
+ iteration_n = str(self._iteration)
+ print(f'\r{prefix} {iteration_n}', end='\r')
+
+ self.new_pathway_parameters={**self._PathwayParameters, **{self._target_parameter:mypathway.defaultParameters[self._target_parameter]*self._seed}}
+ self.obs_ratio = calc_ratio(self)
+
+ if self.obs_ratio == self._expratio:
+ self._lst_ratio.append(self.obs_ratio)
+ self._lst_seed.append(self._seed)
+ self._fold=round(self._seed, abs(decimal.Decimal(str(self._expratio).rstrip('0')).as_tuple().exponent))
+ print('\n\nDONE!\n', '\nRatio: '+str(self.obs_ratio), '\nFOLD: '+str(self._fold), '\nNumber of iterations: '+str(self._iteration))
+ break
+ elif self.obs_ratio < self._expratio:
+
+ self._lst_ratio.append(self.obs_ratio)
+ self._lst_seed.append(self._seed)
+ self._iteration+=1
+ self._seed += self._seed_incrementor
+
+ else:
+ self._lst_ratio.append(self.obs_ratio)
+ self._lst_seed.append(self._seed)
+
+
+ self._iteration+=1
+ self._seed -= self._seed_decrementor
+
+ return
+
+ def PlotIterations(self, save=False, filename=None):
+ '''
+ Plot iterations.
+ '''
+ import plotly.offline as pyoff
+ #dependencies
+ if self._iteration == None: raise TypeError('Simulation data not exist. simulation.fitModel.run() must be run first.')
+
+ #import plotly
+ import plotly.graph_objs as go
+
+ iterations = np.arange(1,self._iteration+1)
+
+ trace=dict(type='scatter', x=self._lst_seed, y=self._lst_ratio, mode='markers',
+ marker=dict(color= iterations, colorscale='Bluered_r', size=14, colorbar=dict(thickness=20, title='iteration number')))
+ #axis_style=dict(zeroline=False, showline=True, mirror=True)
+ layout = dict(title = '',
+ xaxis = dict(
+ title = 'seed',
+ titlefont=dict(
+ size=20
+ ),
+ tickfont=dict(
+ size=20
+ )),
+ yaxis = dict(
+ title = '['+self._observable+']' + ' ratio',
+ titlefont=dict(
+ size=20),
+ tickfont=dict(
+ size=20)
+
+ ),
+ legend=dict(font=dict(size=15)),
+ autosize=False,
+ width=850,
+ height=650
+ )
+
+ fig = go.Figure(data=[trace], layout=layout)
+ if save==True:
+ if filename==None:
+ filename='plot.html'
+ return pyoff.plot(fig, filename=filename)
+ else:
+ ext = os.path.splitext(filename)[-1]
+ if ext == '.png': fig.write_image(filename, scale=3)
+ elif ext == '.html': pyoff.plot(fig, filename=filename)
+ else: raise TypeError("extension not valid. Use png or html.")
+ elif save ==False: return fig
+ return fig
+
+ def ShowGraphs(self, save=False, filename=None):
+ '''
+ Plot the amount of obeservable in function of time, Amplitude, Area Under the Curve, and Full Width at Half Maximum.
+
+ :parameter save: Optional (kwarg boolean): default False
+ :parameter filename: Optional (kwarg str)
+ '''
+
+ from IPython.core.display import display, HTML
+ display(HTML("<style>.container { width:90% !important; }</style>"))
+
+
+ from plotly.subplots import make_subplots
+ from scipy.signal import peak_widths
+ from sklearn import metrics
+ import plotly.offline as pyoff
+
+
+ half_1 = peak_widths(self._obs_curve_1, self._obs_peaks_1, rel_height=0.5)
+ half_2 = peak_widths(self._obs_curve_2, self._obs_peaks_2, rel_height=0.5)
+ fwhm_1 = self._time[int(half_1[3])]-self._time[int(half_1[2])]
+ fwhm_2 = self._time[int(half_2[3])]-self._time[int(half_2[2])]
+
+
+ fig = make_subplots(rows=2, cols=2,vertical_spacing=0.15,
+ subplot_titles=("{} concentration".format(self._observable), "Amplitude", "Area under the curve", "Full Width at Half Maximum"))
+
+ ####################
+ #### MAIN PLOT ####
+ ####################
+ fig.add_trace(go.Scatter(x=self._time, y=self._obs_curve_1*1E3, name='control'), row=1, col=1)
+ fig.add_trace(go.Scatter(x=self._time, y=self._obs_curve_2*1E3, name='{}-fold'.format(self._fold)), row=1, col=1)
+ fig.add_trace(go.Scatter(x=self._time[self._obs_peaks_1], y=self._obs_curve_1[self._obs_peaks_1]*1E3,
+ name='max value', showlegend=False, mode='markers',
+ marker=dict(symbol='x', size=13, color='Black')), row=1,col=1)
+ fig.add_trace(go.Scatter(x=self._time[self._obs_peaks_2], y=self._obs_curve_2[self._obs_peaks_2]*1E3,
+ name='max value', showlegend=False, mode='markers',
+ marker=dict(symbol='x', size=13, color='Black')), row=1,col=1)
+ fig.add_shape(type='line', x0=self._time[int(half_1[2])],y0=half_1[1][0]*1E3, x1=self._time[int(half_1[3])], y1=half_1[1][0]*1E3,
+ line=dict(color='Blue',dash='dash'),xref='x',yref='y', row=1, col=1)
+ fig.add_shape(type='line', x0=self._time[int(half_2[2])],y0=half_2[1][0]*1E3, x1=self._time[int(half_2[3])], y1=half_2[1][0]*1E3,
+ line=dict(color='Red',dash='dash'),xref='x',yref='y', row=1, col=1)
+
+ # Update xaxis properties
+ fig.update_xaxes(title_text="Time (s)", showgrid=False, row=1, col=1, titlefont=dict(size=18),
+ linecolor='black', linewidth=2,
+ ticks='inside', tickfont=dict(size=18), tickcolor='black', ticklen=10, tickwidth=2)
+
+ fig.update_yaxes(title_text=self._observable+' (nM)', titlefont=dict(size=18), showgrid=False, row=1, col=1,
+ linecolor='black', linewidth=2,
+ ticks='inside', tickfont=dict(size=18), tickcolor='black', ticklen=10, tickwidth=2)
+
+
+ ####################
+ #### AMPLITUDE ####
+ ####################
+
+ AMP_labels = [1,2]
+ AMP_values = [self._vmax_obs_curve_1, self._vmax_obs_curve_2]
+ fig.add_trace(go.Bar(x=AMP_labels,y=AMP_values, width = [0.35,0.35], showlegend=False, marker_color='black', name=''), row=1, col=2 )
+
+
+
+ # Update xaxis properties
+ fig.update_xaxes(row=1, col=2, showgrid=False, linecolor='black', linewidth=2, range=[0,3],
+ tickmode='array', tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
+
+ fig.update_yaxes(showgrid=False, range=[round((min(AMP_values)-min(AMP_values)*0.5)/5)*5,round((max(AMP_values)+max(AMP_values)*0.5)/5)*5 ], row=1, col=2,
+ title_text=self._observable+' (nM)', titlefont=dict(size=18),
+ linecolor='black', linewidth=2, ticks='inside', ticklen=10, tickwidth=2, tickfont=dict(size=18))
+
+ # Add diff lines
+ AMP_diffs = [max(AMP_values) - v for v in AMP_values]
+ AMP_diff_labels = dict(zip(AMP_labels, AMP_diffs))
+ fig.add_trace(go.Scatter(name='',x=[1,1.5,2], y=[max(AMP_values)+(max(AMP_values)*0.3)]*3, mode = 'lines+text',showlegend=False, line=dict(color='black', width=1),text=['', 'diff. = {} nM'.format(round(AMP_diffs[0], 3)),''], textposition='top center'), row=1, col=2)
+ fig.add_trace(go.Scatter(name='',x=[AMP_labels[0]-0.175, AMP_labels[0]+0.175], y=[AMP_values[0]+(AMP_values[0]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
+ fig.add_trace(go.Scatter(name='',x=[AMP_labels[1]-0.175, AMP_labels[1]+0.175], y=[AMP_values[1]+(AMP_values[1]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
+ fig.add_trace(go.Scatter(name='',x=[AMP_labels[0], AMP_labels[0]], y=[AMP_values[0]+(AMP_values[0]*0.03), max(AMP_values)+(max(AMP_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
+ fig.add_trace(go.Scatter(name='',x=[AMP_labels[1], AMP_labels[1]], y=[AMP_values[1]+(AMP_values[1]*0.03), max(AMP_values)+(max(AMP_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
+
+
+ ####################
+ #### AUC ####
+ ####################
+
+ # Data
+ AUC_labels = [1,2]
+ AUC_values = [round(metrics.auc(self._time, self._obs_curve_1),2), round(metrics.auc(self._time, self._obs_curve_2),2)]
+ fig.add_trace(go.Bar(x=AUC_labels,y=AUC_values, width = [0.35,0.35], showlegend=False, marker_color='black', name=''), row=2, col=1 )
+
+ # Update xaxis properties
+ fig.update_xaxes(row=2, col=1, tickmode='array', showgrid=False, range=[0,3], linecolor='black', linewidth=2,
+ tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
+
+ fig.update_yaxes(row=2, col=1,showgrid=False, title_text=self._observable+' (nM)', range=[round((min(AUC_values)-min(AUC_values)*0.5)/5)*5,round((max(AUC_values)+max(AUC_values)*0.5)/5)*5],
+ titlefont=dict(size=18),linecolor='black', linewidth=2,
+ ticks='inside', tickfont=dict(size=18),ticklen=10, tickwidth=2)
+
+ # Add diff lines
+ AUC_diffs = [max(AUC_values) - v for v in AUC_values]
+ AUC_diff_labels = dict(zip(AUC_labels, AUC_diffs))
+ fig.add_trace(go.Scatter(name='',x=[1,1.5,2], y=[max(AUC_values)+(max(AUC_values)*0.3)]*3, mode = 'lines+text',showlegend=False,
+ line=dict(color='black', width=1), text=['', 'diff. = {} nM'.format(round(AUC_diffs[0], 3)),''], textposition='top center'), row=2, col=1)
+ fig.add_trace(go.Scatter(name='',x=[AUC_labels[0]-0.175, AUC_labels[0]+0.175], y=[AUC_values[0]+(AUC_values[0]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
+ fig.add_trace(go.Scatter(name='',x=[AUC_labels[1]-0.175, AUC_labels[1]+0.175], y=[AUC_values[1]+(AUC_values[1]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
+ fig.add_trace(go.Scatter(name='',x=[AUC_labels[0], AUC_labels[0]], y=[AUC_values[0]+(AUC_values[0]*0.03), max(AUC_values)+(max(AUC_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
+ fig.add_trace(go.Scatter(name='',x=[AUC_labels[1], AUC_labels[1]], y=[AUC_values[1]+(AUC_values[1]*0.03), max(AUC_values)+(max(AUC_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
+
+
+ ####################
+ #### FWHM ####
+ ####################
+ # Data
+ FWHM_labels = [1,2]
+ FWHM_values = [fwhm_1, fwhm_2]
+ fig.add_trace(go.Bar(x=FWHM_labels,y=FWHM_values, width = [0.35,0.35], showlegend=False,marker_color='black', name=''), row=2, col=2 )
+
+ # Update xaxis properties
+ fig.update_xaxes(row=2, col=2, showgrid=False, range=[0,3], linecolor='black', linewidth=2,
+ tickmode='array', tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
+
+ fig.update_yaxes(row=2, col=2, showgrid=False, range=[self.pathway_parameters['time_in'],round((max(FWHM_values)+(max(FWHM_values)-self.pathway_parameters['time_in'])*0.5)/5)*5],
+ title_text='Time (s)', titlefont=dict(size=18), linecolor='black', linewidth=2,
+ ticks='inside', ticklen=10, tickwidth=2, tickfont=dict(size=18))
+
+ # Add diff lines
+ FWHM_diffs = [max(FWHM_values) - v for v in FWHM_values]
+ FWHM_diff_labels = dict(zip(FWHM_labels, FWHM_diffs))
+ line_height = max(FWHM_values)+((max(FWHM_values)-self.pathway_parameters['time_in'])*0.30)
+ fig.add_trace(go.Scatter(x=[1,1.5,2], y=[line_height]*3, mode = 'lines+text',showlegend=False, line=dict(color='black', width=1),name='',
+ text=['', 'diff. = {} s'.format(round(FWHM_diffs[0], 3)),''], textposition='top center'), row=2, col=2)
+ fig.add_trace(go.Scatter(name='',x=[FWHM_labels[0]-0.175, FWHM_labels[0]+0.175], y=[FWHM_values[0]+(FWHM_values[0]*0.005)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
+ fig.add_trace(go.Scatter(name='',x=[FWHM_labels[1]-0.175, FWHM_labels[1]+0.175], y=[FWHM_values[1]+(FWHM_values[1]*0.005)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
+ fig.add_trace(go.Scatter(name='',x=[FWHM_labels[0], FWHM_labels[0]], y=[FWHM_values[0]+(FWHM_values[0]*0.005), line_height], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
+ fig.add_trace(go.Scatter(name='',x=[FWHM_labels[1], FWHM_labels[1]], y=[FWHM_values[1]+(FWHM_values[1]*0.005), line_height], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
+
+
+ ####################
+ #### FIGURE ####
+ ####################
+
+ fig.update_layout(height=1200, width=1300, title_text="", plot_bgcolor='white',showlegend=True,
+ legend=dict(yanchor="top", x=0.3, y=.99,font=dict(family="sans-serif", size=14,color="black")))
+ fig.update_annotations(font_size=20, font_color='black')
+
+ if save==True:
+ if filename==None:
+ filename='plot.html'
+ return pyoff.plot(fig, filename=filename)
+ else:
+ ext = os.path.splitext(filename)[-1]
+ if ext == '.png': fig.write_image(filename, scale=3)
+ elif ext == '.html': pyoff.plot(fig, filename=filename)
+ else: raise TypeError("extension not valid. Use png or html.")
+ elif save ==False: return fig
+
+ return
+
+
diff --git a/ssbtoolkit/__init__.py b/ssbtoolkit/__init__.py
deleted file mode 100644
index 4e42005..0000000
--- a/ssbtoolkit/__init__.py
+++ /dev/null
@@ -1,1414 +0,0 @@
-import pkgutil
-
-__all__ = []
-for loader, module_name, is_pkg in pkgutil.walk_packages(__path__):
- __all__.append(module_name)
- _module = loader.find_module(module_name).load_module(module_name)
- globals()[module_name] = _module
-
-
-import sys, os, warnings
-sys.path.insert(0, os.path.abspath(os.path.split(os.path.realpath(__file__))[0]))
-warnings.simplefilter(action='ignore')
-
-
-
-
-import platform, site
-distpath = site.getsitepackages()[0]
-if platform.system() == 'Linux':
- BioNetGen=os.path.join(distpath, 'bionetgen/bng-linux:')
-elif platform.system() == 'Darwin':
- BioNetGen=os.path.join(distpath, 'bionetgen/bng-mac:')
-elif platform.system()=='Windows':
- BioNetGen=os.path.join(distpath, 'bionetgen/bng-win:')
-else:
- raise ValueError('BioNetGen error. Platform unknown! The pygomodo was tested in Linux and Darwin (Mac) platforms.')
-os.environ['PATH']=BioNetGen+os.environ['PATH']
-
-
-#LIBRARIES
-import importlib
-import pylab as pl
-from pysb.simulator import ScipyOdeSimulator
-from scipy.optimize import curve_fit
-from sklearn.preprocessing import minmax_scale
-import plotly.graph_objs as go
-import pandas as pd
-import numpy as np
-import ssbtoolkit.Utils as utils
-
-pathways_path=(os.path.join(os.path.split(os.path.realpath(__file__))[0], 'pathways'))
-
-######MAIN CODE#####
-class Binding:
- """This class simulate ligand-target binding curves."""
- def __init__(self):
- self.receptor_conc = None
- self.lig_conc_range = None
- self.pKd = None
- self.submax_concentration = None
-
- def Bind(self, **kwargs):
- """
- Applies an function to calculate the fraction of occupited receptors at equilibrium.
-
- :parameter receptor_conc: Required (kwarg flt): concentration of receptor
- :parameter lig_conc_range: Required (kwarg array): array of range of ligand concentration
- :parameter pKd: Required (kwarg flt): pKd value of the ligand
- """
-
- if 'receptor_conc' not in kwargs: raise TypeError("ERROR: receptor_conc is missing")
- if 'lig_conc_range' not in kwargs: raise TypeError("ERROR: lig_conc_range is missing")
- if 'pKd' not in kwargs: raise TypeError("ERROR: pKd is missing")
-
- self._receptor_conc = kwargs.pop('receptor_conc')
- self._lig_conc_range = kwargs.pop('lig_conc_range')
- self._pKd = kwargs.pop('pKd')
-
- binding_data=[]
- for conc in self._lig_conc_range:
- binding_data.append(utils.CalcOccupancy(self._receptor_conc, conc, 0, self._pKd, 0))
- self.binding_data=binding_data
- return self.binding_data
-
- def SubMaxConcentration(self):
- """
- Calculates the maximum bending point of a sigmoid-shaped curve according to the mathod of Sebaugh et al., 2003.
-
- :parameter drug_receptor: Required (int): concentration of the receptor
- :parameter lig_conc_range: Required (array): array of a range of ligand concentration
- :return: instance .submax_concentration (flt)
-
- .. note:: The minimization uses the Nelder-Mead method.
- """
-
- from scipy.optimize import curve_fit, minimize
-
-
- def sigmoid(X, Bottom, Top, Kd, p):
- return Bottom + (Top-Bottom)/(1+np.power((Kd/X),p))
-
- xfit = np.geomspace(np.min(self._lig_conc_range), np.max(self._lig_conc_range), 50000) #warning: shoud this be the minimum and maximum of concentration
- popt, pcov = curve_fit(sigmoid, self._lig_conc_range, self.binding_data, bounds=([np.min(self.binding_data),-np.inf,-np.inf, 0.5],[np.inf,np.max(self.binding_data),np.inf, 2.5]))
-
-
- def sigmoid_deriv_b(x, a,d,c,b):
- return (x/c)**b*(a - d)*np.log(x/c)/((x/c)**b + 1)**2
-
- min_value = minimize(sigmoid_deriv_b, np.max(xfit), args=(popt[0],popt[1],popt[2],popt[3]), method = 'Nelder-Mead')
-
- self.submax_concentration = round(min_value.x[0],3)
- return self.submax_concentration
-
- def ShowCurve(self):
- """
- Plots ligand-target binding curve
- """
-
- #import plotly
- import plotly.graph_objs as go
- from scipy.optimize import curve_fit
- from sklearn.preprocessing import minmax_scale
-
- ##Fitting curve to the data
-
- yy = minmax_scale(self.binding_data)*100
-
- def equation_dose(X, Bottom, Top, EC50, p):
- return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
-
- popt, pcov = curve_fit(equation_dose, self._lig_conc_range, yy, bounds=([np.min(yy),-np.inf,-np.inf, 0.5],[np.inf,np.max(yy),np.inf, 2.5]))
-
- xfit = np.geomspace(np.min(self._lig_conc_range), np.max(self._lig_conc_range), 50000) # These values are the same as the values for the simulation time and not ligand concentration
- yfit = minmax_scale(equation_dose(xfit, *popt))*100
-
-
-
- trace1 = go.Line(x=xfit, y=yfit, showlegend=False, name='radioligand')
- if self.submax_concentration:
- xsubmaximal = np.array(self.submax_concentration)
- ysubmaximal = np.array(equation_dose(xsubmaximal, *popt))
- trace2 = go.Scatter(x=xsubmaximal, y=ysubmaximal, showlegend=True, mode='markers', name='submaximal ({} μM)'.format(xsubmaximal),
- marker=dict(size=14))
- else: trace2=[]
-
-
- layout = dict(title = '',
- xaxis = dict(
- title = '[ligand] μM',
- type ='log',
- exponentformat='e',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '% occupied receptors',
-
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650,
- )
- if self.submax_concentration:
- fig = go.Figure(data=[trace1, trace2], layout=layout)
- else:
- fig = go.Figure(data=[trace1], layout=layout)
- return fig
-
-class Simulation:
- """
- This class simulates the mathematical models of the signaling pathways.
- """
- class Activation:
- """
- Simulation of the activation of signaling pathways (i.e. activation by agonists)
- """
- def __init__(self):
- self._ligands=None
- self._affinities=None
- self._pathway=None
- self._receptor_conc=None
- self._lig_conc_range=None
- self._ttotal=None
- self._nsteps=None
- self._binding_kinetics=True
- self._binding_kinetic_parameters=None
- self.simulation_data=None
- self.processed_data=None
-
- def SetSimulationParameters(self, **kwargs):
- """
- :parameter ligands: Required (kwargs list): list of ligands' names (str)
- :parameter affinities: Required (kwargs list): list of pKd values (flt)
- :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
- :parameter receptor_conc: Required (kwargs flt): receptors concentration (nM)
- :parameter lig_conc_range: Required (kwargs array): range of ligands' concentration
- :parameter ttotal: Required (kwargs int): simulation time (seconds)
- :parameter nsteps: Required (kwargs int): simulation time step
- :parameter binding_kinetics: Optional (kwargs boolean): default (False)
-
-
- .. warning:: the order of the lists of ligands names and affinities list must be the same.
-
- """
- self._ligands= kwargs.pop('ligands')
- if 'affinities' in kwargs:
- self._affinities=kwargs.pop('affinities')
- self._pathway=kwargs.pop('pathway')
- self._receptor_conc=kwargs.pop('receptor_conc')
- self._lig_conc_range=kwargs.pop('lig_conc_range')
- self._ttotal=kwargs.pop('ttotal')
- self._nsteps=kwargs.pop('nsteps')
- self._binding_kinetics=kwargs.pop('binding_kinetics')
- if 'binding_kinetic_parameters' in kwargs:self._binding_kinetic_parameters=kwargs.pop('binding_kinetic_parameters')
- self._DefaultPathwayParametersDataFrame=pd.DataFrame()
-
- return
-
- def PathwayParameters(self):
- """
- Display table with default pathway parameters.
-
- .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
-
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def UserPathwayParameters(self, path):
- """
- Import user pathway parameters.
-
- :parameter path: Required (kwarg str): directory path
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def PathwayParametersToCSV(self, path):
- """
- Export pathway parameters into CSV format.
-
- :parameter path: Required (kwarg str): directory path
- """
- self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
- print('saved in:', path)
- return
-
- def Reactions(self):
- """
- Display pathway reactions.
- """
- from IPython.display import display, HTML
- display(HTML("<style>.container {width:90% !important}</style>"))
- return pd.read_csv(pathways_path+'/{}_reactions.csv'.format(self._pathway))
-
- def Run(self):
- '''
- This function runs the pathway simulation and returns the raw simulation data.
- '''
-
- #Check inputs
- if self._ligands==None: raise TypeError("ligands list undefined.")
- elif self._pathway==None: raise TypeError("pathway name undefined.")
- elif self._binding_kinetics==False and self._affinities==None: raise TypeError("affinity_values_dict undefined.")
- elif self._binding_kinetics==True and self._affinities==None: pass
- elif self._lig_conc_range.any() == False: raise TypeError("lig_conc_range undefined.")
- elif self._ttotal==None: raise TypeError("ttotal undefined.")
- elif self._nsteps==None: raise TypeError("nsteps undefined.")
- elif self._receptor_conc==None: raise TypeError("receptor_conc undefined.")
- else: pass
-
-
- #Check Pathway availability and import it
- available_pathways = ['Gs', 'Gi', 'Gq']
- if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
- if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Pathways available: "Gs", "Gi", "Gq".')
- mypathway = importlib.import_module('.'+self._pathway, package='ssbtoolkit.pathways')
-
- #Get default pathway parameters
- if self._DefaultPathwayParametersDataFrame.empty and self._binding_kinetic_parameters==None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self._binding_kinetic_parameters is None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
- except:
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty and self._binding_kinetic_parameters is not None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self._binding_kinetic_parameters is not None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = {**newparameters.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters}
- except:
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters}
-
- #Input
- t = pl.geomspace(0.00001, self._ttotal, num=self._nsteps) # (a,b,c); a is the starting time ; b is the total time simulated ; c is the number of points
-
-
- #Output
- simulation_data={}
-
- #Function
- for ligand in self._ligands:
- ligand_name = os.path.splitext(str(ligand))[0]
- data=[]
- utils.PrintProgressBar(0, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
- ###DANGER ZONE###
- if self._binding_kinetic_parameters is not None:
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters[self._ligands.index(ligand)]}
- ######################
-
- for idx in range(len(self._lig_conc_range)):
-
- ligand_conc = self._lig_conc_range[idx]
- if self._binding_kinetics == False:
- #get LR conc
- parameters = {**self._PathwayParameters, 'R_init':self._receptor_conc}
- LR_conc_init = utils.CalcOccupancy(self._receptor_conc, ligand_conc, 0, self._affinities[self._ligands.index(ligand)], 0)
- mymodel = mypathway.network(LR=LR_conc_init, kinetics=False, **parameters)
- simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
- yout = simres.all
-
- elif self._binding_kinetics == True:
- parameters={**self._PathwayParameters,'R_init':self._receptor_conc, 'L_init':self._lig_conc_range[idx] }
- mymodel = mypathway.network(kinetics=True, **parameters)
- simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
- yout = simres.all
-
- d1={'ligand_conc':ligand_conc, 'time':t }
-
- for idx2 in range(len(mypathway.list_of_observables)):
- d2={mypathway.list_of_observables[idx2]:yout[mypathway.list_of_observables[idx2]]}
- d1.update(d2)
- data.append(d1)
- utils.PrintProgressBar(idx + 1, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
-
- simulation_data[ligand_name] = {'sim_data':data,
- 'label':ligand_name,}
- self.simulation_data = simulation_data
- return
-
- def Analysis(self):
- '''
- This function calculates the dose-response effect.
-
- :return: instance of processed_data
- '''
-
- if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.activation.run() must be run first.')
-
- # Define all the lists and dictionaries used in this function
- raw_data=[]
- normalized_data=[]
- dose={}
-
- #defining concentration range
- lig_conc_min = self._lig_conc_range.min()
- lig_conc_max = self._lig_conc_range.max()
-
- #Main function
- for ligand in self.simulation_data:
-
- #definig and dictionaries used in this loop:
- raw_data_dict={}
- normalized_data_dict={}
-
- # Calculate dose-response curve
- #get metabolite concentration, rescale, and transform data if pathway/metabolite decrease
- # metabolite_raw is not normalized
- if self._pathway == 'Gi' or self._pathway == 'Gz(Gi)':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(1-np.array(metabolite_conc_raw))
- elif self._pathway == 'Gs':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- elif self._pathway == 'Gq':
- metabolite='IP3'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- else: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
-
-
- ## save results
- raw_data_dict['x']=self._lig_conc_range
- raw_data_dict['y']=metabolite_conc_raw
- raw_data_dict['label']=self.simulation_data[ligand]['label']
-
- normalized_data_dict['x']=self._lig_conc_range
- normalized_data_dict['y']=metabolite_conc_norm
- normalized_data_dict['label']=self.simulation_data[ligand]['label']
-
- ## create a list of all data
- raw_data.append(raw_data_dict)
- normalized_data.append(normalized_data_dict)
-
- ##Fitting curve to the data
-
- def equation_dose(X, Bottom, Top, EC50, p):
- return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
-
- popt_EC50, pcov = curve_fit(equation_dose, self._lig_conc_range, metabolite_conc_norm, bounds=([np.min(metabolite_conc_norm),-np.inf,-np.inf, 0.5],[np.inf,np.max(metabolite_conc_norm),np.inf, 2.5]))
-
- xfit_EC50 = np.geomspace(lig_conc_min, lig_conc_max, 50000) # These values are the same as the values for the simulation time and not ligand concentration
- yfit_EC50 = equation_dose(xfit_EC50, *popt_EC50)
-
- fit_EC50={'x':xfit_EC50, 'y':yfit_EC50, 'label':self.simulation_data[ligand]['label']}
-
- dose[ligand] = {'raw_data': raw_data_dict,
- 'normalized_data':normalized_data_dict ,
- 'fitted_data': fit_EC50,
- 'EC50 (μM)': round(popt_EC50[2],5),
- 'pEC50': round(-np.log10(popt_EC50[2]*1E-6),2)}
-
- self.processed_data=dose
- return
-
- def ShowCurve(self, save=False, filename=None):
- '''
- Plots the dose-response curve.
- '''
-
- if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.activation.run() must be run first.')
-
- import plotly
- import plotly.graph_objs as go
- import plotly.offline as pyoff
-
- colors = plotly.colors.DEFAULT_PLOTLY_COLORS
-
- plot_data=[]
-
- color_id=0
- for ligand in self.processed_data:
- trace_norm = go.Scatter(x=self.processed_data[ligand]['normalized_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['normalized_data']['y'])*100 ,
- mode='markers',
- showlegend=True,
- name=self.processed_data[ligand]['normalized_data']['label'],
- marker=dict(color=colors[color_id]))
- plot_data.append(trace_norm)
-
- trace_fitted = go.Scatter(x=self.processed_data[ligand]['fitted_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['fitted_data']['y'])*100,
- mode='lines',
- showlegend=False,
- name=self.processed_data[ligand]['fitted_data']['label'],
- line=dict(color=colors[color_id]))
- plot_data.append(trace_fitted)
- color_id +=1
-
- layout = dict(title = '',
- xaxis = dict(
- title = '[ligand] μM',
- type ='log',
- #range = [-3, 2],
- exponentformat='e',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '% Response',
- #range = [0, 100],
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650,
- )
-
- fig = go.Figure(data=plot_data, layout=layout)
- #fig['layout']['yaxis'].update(autorange = True)
-
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
- return
-
- def ShowPotency(self):
- '''
- Return the potency values as a pandas DataFrame.
- '''
- import pandas as pd
- data = Simulation.Activation.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- return df
-
- def PotencyToDict(self):
- '''
- Convert potencies into a dictionary.
- '''
-
- #dependencies
- if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.activation.analysis() must be run first.')
-
- kvalues={}
- for ligand in self.processed_data:
- IC50 = list(self.processed_data[ligand].keys())[-2]
- IC50_value = self.processed_data[ligand][IC50]
- pIC50 = list(self.processed_data[ligand].keys())[-1]
- pIC50_value = self.processed_data[ligand][pIC50]
- kvalues[ligand]={IC50:IC50_value, pIC50:pIC50_value}
- return kvalues
-
- def PotencyToCSV(self, path):
- '''
- Exports the potency values into csv format.
-
- :parameter path: Required (kwarg str): directory path to save the csv file
- '''
-
- data = Simulation.Activation.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- df.to_csv(path, index=False)
- return
-
- class Inhibition:
- """
- Simulation of the inhibition of signaling pathways (i.e. inhibition by antagonists).
- """
- def __init__(self):
- self._agonist=None
- self._agonist_affinity=None
- self._agonist_submaximal_conc=None
- self._antagonists=None
- self._antagonists_affinities=None
- self._pathway=None
- self._receptor_conc=None
- self._lig_conc_range=None
- self._ttotal=None
- self._nsteps=None
- self._binding_kinetics=False
- self._binding_kinetic_parameters=None
- self.simulation_data=None
- self.processed_data=None
-
- def SetSimulationParameters(self, **kwargs):
- """
- :parameter agonist: Required (kwargs str): agonist name
- :parameter agonist_affinity: Required (kwargs flt): agonist pKd value
- :parameter agonist_submaximal_conc: Required (kwargs flt): agonist submaximal concentration
- :parameter antagonists: Required (kwargs list):list of antagonists names (str)
- :parameter antagonists_affinities: Required (kwargs list): list of antagonists affinity values (flt)
- :parameter antagonists_conc_range: Required (kwargs array): range of ligands' concentration (nM)
- :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
- :parameter receptor_conc: Required (kwargs flt): receptors concentration (nM)
- :parameter ttotal: Required (kwargs int): simulation time (seconds)
- :parameter nsteps: Required (kwargs int): simulation time step
- :parameter kinetics: Optional (kwargs boolean): default (False)
-
-
- :return: instances of all parameters
-
- .. warning:: the order of the lists of the antagonists names and affinities list must be the same.
-
- """
- self._agonist= kwargs.pop('agonist')
- self._agonist_affinity=kwargs.pop('agonist_affinity')
- self._agonist_submaximal_conc=kwargs.pop('agonist_submaximal_conc')
- self._antagonists=kwargs.pop('antagonists')
- self._antagonists_affinities=kwargs.pop('antagonists_affinities')
- self._pathway=kwargs.pop('pathway')
- self._receptor_conc=kwargs.pop('receptor_conc')
- self._lig_conc_range=kwargs.pop('lig_conc_range')
- self._ttotal=kwargs.pop('ttotal')
- self._nsteps=kwargs.pop('nsteps')
- if 'kinetics' in kwargs:
- self._binding_kinetics=kwargs.pop('kinetics')
- if self._binding_kinetics==True: raise TypeError("The of Kinetic parameters during an inhibition simulation it is not supported yet.")
- else: self._binding_kinetics=False
- if 'binding_kinetic_parameters' in kwargs:
- self._binding_kinetic_parameters=kwargs.pop('binding_kinetic_parameters')
- self._DefaultPathwayParametersDataFrame=pd.DataFrame()
-
- return
-
- def PathwayParameters(self):
- """
- Display table with default pathway parameters.
-
- .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
-
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def UserPathwayParameters(self, path):
- """
- Import user pathway parameters.
-
- :parameter path: Required (kwarg str): directory path
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def PathwayParametersToCSV(self, path):
- """
- Export pathway parameters into CSV format.
-
- :parameter path: Required (kwarg str): directory path
- """
- self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
- print('saved in:', path)
- return
-
- def Reactions(self):
- """
- Display pathway reactions.
- """
- from IPython.display import display, HTML
- display(HTML("<style>.container {width:90% !important}</style>"))
- return pd.read_csv(pathways_path+'/{}_reactions.csv'.format(self._pathway))
-
- def Run(self):
- '''
- This function runs the pathway simulation and returns the raw simulation data.
- '''
-
- #Check inputs
- if self._agonist==None: raise TypeError("agonist undefined.")
- elif self._agonist_affinity==None: raise TypeError("agonist_affinity undifined.")
- elif self._antagonists==None: raise TypeError("antagonists list undefined.")
- elif self._antagonists_affinities==None: raise TypeError("antagonists affinity values undefined.")
- elif self._pathway==None: raise TypeError("pathway undefined.")
- elif self._lig_conc_range.any() == False: raise TypeError("lig_conc_range undefined.")
- elif self._agonist_submaximal_conc == None: raise TypeError("agonist_submaximal_conc undifined.")
- elif self._ttotal==None: raise TypeError("ttotal undefined.")
- elif self._nsteps==None: raise TypeError("nsteps undefined.")
- elif self._receptor_conc==None: raise TypeError("receptor_conc undefined.")
- elif self._binding_kinetics==True: raise TypeError("The of Kinetic parameters during an inhibition simulation it is not supported yet.")
- else: pass
-
- #check pathway
- available_pathways = ['Gs', 'Gi', 'Gq']
- if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
- if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
- mypathway = importlib.import_module('.'+self._pathway, package='ssbtoolkit.pathways')
-
- #Get default pathway parameters
- if self._DefaultPathwayParametersDataFrame.empty:
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty is False:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
- except:
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- #Input
- t = pl.geomspace(0.00001, self._ttotal, num=self._nsteps) # (a,b,c); a is the starting time ; b is the total time simulated ; c is the number of points
-
- #Output
- simulation_data={}
-
- #Function
- for ligand in self._antagonists:
- ligand_name = os.path.splitext(ligand)[0]
- data=[]
- utils.PrintProgressBar(0, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
- for idx in range(len(self._lig_conc_range)):
-
- ligand_conc = self._lig_conc_range[idx]
-
- #get LR conc
- parameters = {**self._PathwayParameters, 'R_init':self._receptor_conc}
- LR_conc_init = utils.CalcOccupancy(self._receptor_conc, self._agonist_submaximal_conc, ligand_conc, self._agonist_affinity, self._antagonists_affinities[self._antagonists.index(ligand)])
- mymodel = mypathway.network(LR=LR_conc_init, kinetics=False, **parameters)
- simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
- yout = simres.all
-
- d1={'ligand_conc':ligand_conc, 'time':t }
-
- for idx2 in range(len(mypathway.list_of_observables)):
- d2={mypathway.list_of_observables[idx2]:yout[mypathway.list_of_observables[idx2]]}
- d1.update(d2)
- data.append(d1)
- utils.PrintProgressBar(idx + 1, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
- simulation_data[ligand_name] = {'sim_data':data,
- 'label':self._agonist+' + ' + ligand_name}
-
- self.simulation_data=simulation_data
- return
-
- def Analysis(self):
- '''
- This function calculates the dose-response effect.
-
- :return: instance processed_data
- '''
- #dependencies
- if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.inhibition.run() must be run first.')
-
- # Define all the lists and dictionaries used in this function
- raw_data=[]
- normalized_data=[]
- dose={}
-
- #defining concentration range
- #defining concentration range
- lig_conc_min = self._lig_conc_range.min()
- lig_conc_max = self._lig_conc_range.max()
-
- #Main function
- for ligand in self.simulation_data:
-
- #definig and dictionaries used in this loop:
- raw_data_dict={}
- normalized_data_dict={}
-
- # Calculate dose-response curve
- #get metabolite concentration, rescale, and transform data if pathway/metabolite decrease
- # metabolite_raw is not normalized
- if self._pathway == 'Gi' or self._pathway == 'Gz(Gi)':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(1-np.array(metabolite_conc_raw))
- elif self._pathway == 'Gs':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- elif self._pathway == 'Gq':
- metabolite='IP3'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- else: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
-
-
- ## save results
- raw_data_dict['x']=self._lig_conc_range
- raw_data_dict['y']=metabolite_conc_raw
- raw_data_dict['label']=self.simulation_data[ligand]['label']
-
- normalized_data_dict['x']=self._lig_conc_range
- normalized_data_dict['y']=metabolite_conc_norm
- normalized_data_dict['label']=self.simulation_data[ligand]['label']
-
- ## create a list of all data
- raw_data.append(raw_data_dict)
- normalized_data.append(normalized_data_dict)
-
- ##Fitting curve to the data
-
- def equation_dose(X, Bottom, Top, EC50, p):
- return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
-
- popt_IC50, pcov = curve_fit(equation_dose, self._lig_conc_range, metabolite_conc_norm, bounds=([np.min(metabolite_conc_norm),-np.inf,-np.inf, 0.5],[np.inf,np.max(metabolite_conc_norm),np.inf, 2.5]))
-
- xfit_IC50 = np.geomspace(lig_conc_min, lig_conc_max, 50000) # These values are the same as the values for the simulation time and not ligand concentration
- yfit_IC50 = equation_dose(xfit_IC50, *popt_IC50)
-
- fit_IC50={'x':xfit_IC50, 'y':yfit_IC50, 'label':self.simulation_data[ligand]['label']}
-
- dose[ligand] = {'raw_data': raw_data_dict,
- 'normalized_data':normalized_data_dict ,
- 'fitted_data': fit_IC50,
- 'IC50 (μM)': round(popt_IC50[2],5),
- 'pIC50': round(-np.log10(popt_IC50[2]*1E-6),2)}
-
- self.processed_data=dose
- return
-
- def ShowCurve(self, save=False, filename=None):
- '''
- Plot the dose-response curve.
- '''
- #dependencies
- if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.inhibition.analysis() must be run first.')
-
- import plotly
- import plotly.graph_objs as go
- import plotly.offline as pyoff
-
- colors = plotly.colors.DEFAULT_PLOTLY_COLORS
-
- plot_data=[]
-
- color_id=0
- for ligand in self.processed_data:
- trace_norm = go.Scatter(x=self.processed_data[ligand]['normalized_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['normalized_data']['y'])*100 ,
- mode='markers',
- showlegend=True,
- name=self.processed_data[ligand]['normalized_data']['label'],
- marker=dict(color=colors[color_id]))
- plot_data.append(trace_norm)
-
- trace_fitted = go.Scatter(x=self.processed_data[ligand]['fitted_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['fitted_data']['y'])*100,
- mode='lines',
- showlegend=False,
- name=self.processed_data[ligand]['fitted_data']['label'],
- line=dict(color=colors[color_id]))
- plot_data.append(trace_fitted)
- color_id +=1
-
- layout = dict(title = '',
- xaxis = dict(
- title = '[ligand] μM',
- type ='log',
- #range = [-4, 2],
- exponentformat='e',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '% Response',
- #range = [0, 100],
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650,
- )
-
- fig = go.Figure(data=plot_data, layout=layout)
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
- return
-
- def ShowPotency(self):
- '''
- Return the potency values as a pandas DataFrame.
- '''
- import pandas as pd
- data = Simulation.Inhibition.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- return df
-
- def PotencyToDict(self):
- '''
- Convert potencies into a dictionary.
- '''
- #dependencies
- if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.inhibition.analysis() must be run first.')
-
- kvalues={}
- for ligand in self.processed_data:
- IC50 = list(self.processed_data[ligand].keys())[-2]
- IC50_value = self.processed_data[ligand][IC50]
- pIC50 = list(self.processed_data[ligand].keys())[-1]
- pIC50_value = self.processed_data[ligand][pIC50]
- kvalues[ligand]={IC50:IC50_value, pIC50:pIC50_value}
- return kvalues
-
- def PotencyToCSV(self, path):
- '''
- Exports the potency values into csv format.
-
- :parameter path: Required (kwarg str): directory path to save the csv file
- '''
- data = Simulation.Inhibition.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- df.to_csv(path, index=False)
- return
-
- class FitModel:
- """
- Fit a model to experimental data.
-
- .. note:: This class was developed to reproduce data from a specific experimental setup. Please see tutorial 4 (OXTR pathay). Use carefully!
- """
- def __init__(self):
-
- #fitting parameters
- self._expratio = None
- self._seed = None
- self._maxiter = None
- self._seed_incrementor = None
- self._target_parameter = None
-
- #Pathway parameters
- self._ttotal = None
- self._nsteps = None
- self._pathway = None
- self._observable = None
- self.pathway_parameters = {}
-
- def SetSimulationParameters(self, **kwargs):
- """
- :parameter pathway_parameters: Required (kwargs): dict of pathway parameters
- :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
- :parameter ttotal: Required (kwargs int): simulation time (seconds)
- :parameter nsteps: Required (kwargs int): simulation time step
- :parameter observable: Required (kwargs str): molecular specie to be measured
-
- :return: instances of all parameters
-
- """
-
- if 'pathway_parameters' in kwargs:
- self.pathway_parameters = kwargs.pop('pathway_parameters')
- #print('pathway_parameters YES')
- self._DefaultPathwayParametersDataFrame=pd.DataFrame()
- if 'ttotal' in kwargs:
- self._ttotal = int(kwargs.pop('ttotal'))
- print('ttotal =', self._ttotal)
- else: raise TypeError("ttotal undefined.")
-
- if 'nsteps' in kwargs:
- self._nsteps = int(kwargs.pop('nsteps', 1000))
- print('nsteps =', self._nsteps)
-
- if 'pathway' in kwargs:
- self._pathway = str(kwargs.pop('pathway'))
- print('pathway ->', self._pathway)
- else: raise TypeError("pathway undefined.")
-
- available_pathways = ['Gs', 'Gi', 'Gq', 'OXTR_pathway']
- if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
- if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
-
-
- if'observable' in kwargs:
- self._observable = str(kwargs.pop('observable'))
- print('observable ->', self._observable)
- else: raise TypeError("observable undefined.")
-
- return
-
- def PathwayParameters(self):
- """
- Display table with default pathway parameters.
-
- .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
-
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def UserPathwayParameters(self, path):
- """
- Import user pathway parameters.
-
- :parameter path: Required (kwarg str): directory path
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def PathwayParametersToCSV(self, path):
- """
- Export pathway parameters into CSV format.
-
- :parameter path: Required (kwarg str): directory path
- """
- self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
- print('saved in:', path)
- return
-
- def Reactions(self):
- """
- Display pathway reactions.
- """
- from IPython.display import display, HTML
- display(HTML("<style>.container {width:90% !important}</style>"))
- return pd.read_csv(pathways_path+'/{}_reactions.csv'.format(self._pathway))
-
- def Run(self, **kwargs):
- """
- Fits of the model to experimental data.
-
- :parameter expratio: Required (kwargs flt): experimental signalling specie concentration ratio
- :parameter target_parameter: Required (kwargs str):kinetic parameter to me modified
- :parameter maxiter: Required (kwargs int): maximum number of iteration
- :parameter seed: Required (kwargs flt): ramdom seed for scaling the modified parameter
- :parameter seed_incrementor: Required (kwargs flt): seed incrementor (each iteration will increment the seed by this value)
- :parameter seed_decrementor: Required (kwargs flt): seed decrementor (each iteration will decrement the seed by this value)
-
- """
-
- from scipy.signal import find_peaks
- import decimal
- #fitting parameters
- if 'expratio' in kwargs:
- self._expratio = float(kwargs.pop('expratio'))
- print('expratio =', self._expratio)
- else: raise TypeError("exratio undefined.")
-
- if 'seed' in kwargs:
- self._seed = float(kwargs.pop('seed'))
- print('seed =', self._seed)
- else: raise TypeError("seed undefined.")
-
- if 'maxiter' in kwargs:
- self._maxiter = int(kwargs.pop('maxiter', 100))
- print('maxiter =', self._maxiter)
-
- if 'seed_incrementor' in kwargs:
- self._seed_incrementor = float(kwargs.pop('seed_incrementor', 0.1))
- print('seed_incrementor =', self._seed_incrementor)
-
- if 'seed_decrementor' in kwargs:
- self._seed_decrementor = float(kwargs.pop('seed_decrementor', 0.1))
- print('seed_decrementor =', self._seed_decrementor)
-
- if 'target_parameter' in kwargs:
- self._target_parameter = str(kwargs.pop('target_parameter'))
- print('target_parameter ->', self._target_parameter)
- else: raise TypeError("target_parameter undefined.")
-
-
- #Get default pathway parameters
- if self._DefaultPathwayParametersDataFrame.empty and self.pathway_parameters==None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self.pathway_parameters is None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
- except:
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty and self.pathway_parameters is not None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv(pathways_path+'/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self.pathway_parameters is not None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = {**newparameters.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
- except:
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
-
-
-
- #simulation parameters:
- if not self._ttotal:
- raise TypeError("simulation parameters unknown. Set the the simulation parameters first wiht set_simulation_parameters()")
-
- #Main function
- mypathway = importlib.import_module('.'+self._pathway, package='ssbtoolkit.pathways')
- self.simtime = pl.geomspace(0.00001, self._ttotal, num=self._nsteps)
-
- #Simulation 1
- pathway_model = mypathway.network(LR=None, kinetics=True, **self._PathwayParameters)
- sim1 = ScipyOdeSimulator(pathway_model, tspan=self.simtime,compiler='cython').run()
- self.simres1 = sim1.all
-
- def calc_ratio(self):
-
-
- #Simulation 2
- sim2 = ScipyOdeSimulator(mypathway.network(**self.new_pathway_parameters), tspan=self.simtime, compiler='cython').run()
- self.simres2 = sim2.all
-
- #analysis
- obs_name = 'obs_'+self._observable
- obs_1 = self.simres1[obs_name]
- obs_2 = self.simres2[obs_name]
-
- if 'time_in' in self.new_pathway_parameters:
- self._time = np.take(self.simtime, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
- obs_curve_1 = np.take(obs_1, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
- obs_curve_2 = np.take(obs_2, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
-
- else:
- self._time = np.take(self.simtime, np.where(self.simtime>0))[0]
- obs_curve_1 = np.take(obs_1, np.where(self.simtime > 0))[0]
- obs_curve_2 = np.take(obs_2, np.where(self.simtime > 0))[0]
-
- obs_peaks_1, _ = find_peaks(obs_curve_1)
- obs_peaks_2, _ = find_peaks(obs_curve_2)
-
- vmax_obs_curve_1 = obs_curve_1[obs_peaks_1][-1]*1E3
- vmax_obs_curve_2 = obs_curve_2[obs_peaks_2][-1]*1E3
-
- obs_ratio = round(vmax_obs_curve_2/vmax_obs_curve_1, abs(decimal.Decimal(str(self._expratio).rstrip('0')).as_tuple().exponent))
-
- self._obs_curve_1=obs_curve_1
- self._obs_curve_2=obs_curve_2
- self._obs_peaks_1=obs_peaks_1
- self._obs_peaks_2=obs_peaks_2
- self._vmax_obs_curve_1=vmax_obs_curve_1
- self._vmax_obs_curve_2=vmax_obs_curve_2
-
- return obs_ratio
-
- self._iteration=1
- print('\n')
-
- self._lst_ratio=[]
- self._lst_seed=[]
-
- for idx in range(self._maxiter):
-
- prefix = 'iteration'
- iteration_n = str(self._iteration)
- print(f'\r{prefix} {iteration_n}', end='\r')
-
- self.new_pathway_parameters={**self._PathwayParameters, **{self._target_parameter:mypathway.defaultParameters[self._target_parameter]*self._seed}}
- self.obs_ratio = calc_ratio(self)
-
- if self.obs_ratio == self._expratio:
- self._lst_ratio.append(self.obs_ratio)
- self._lst_seed.append(self._seed)
- self._fold=round(self._seed, abs(decimal.Decimal(str(self._expratio).rstrip('0')).as_tuple().exponent))
- print('\n\nDONE!\n', '\nRatio: '+str(self.obs_ratio), '\nFOLD: '+str(self._fold), '\nNumber of iterations: '+str(self._iteration))
- break
- elif self.obs_ratio < self._expratio:
-
- self._lst_ratio.append(self.obs_ratio)
- self._lst_seed.append(self._seed)
- self._iteration+=1
- self._seed += self._seed_incrementor
-
- else:
- self._lst_ratio.append(self.obs_ratio)
- self._lst_seed.append(self._seed)
-
-
- self._iteration+=1
- self._seed -= self._seed_decrementor
-
- return
-
- def PlotIterations(self, save=False, filename=None):
- '''
- Plot iterations.
- '''
- import plotly.offline as pyoff
- #dependencies
- if self._iteration == None: raise TypeError('Simulation data not exist. simulation.fitModel.run() must be run first.')
-
- #import plotly
- import plotly.graph_objs as go
-
- iterations = np.arange(1,self._iteration+1)
-
- trace=dict(type='scatter', x=self._lst_seed, y=self._lst_ratio, mode='markers',
- marker=dict(color= iterations, colorscale='Bluered_r', size=14, colorbar=dict(thickness=20, title='iteration number')))
- #axis_style=dict(zeroline=False, showline=True, mirror=True)
- layout = dict(title = '',
- xaxis = dict(
- title = 'seed',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '['+self._observable+']' + ' ratio',
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650
- )
-
- fig = go.Figure(data=[trace], layout=layout)
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
- return fig
-
- def ShowGraphs(self, save=False, filename=None):
- '''
- Plot the amount of obeservable in function of time, Amplitude, Area Under the Curve, and Full Width at Half Maximum.
-
- :parameter save: Optional (kwarg boolean): default False
- :parameter filename: Optional (kwarg str)
- '''
-
- from IPython.core.display import display, HTML
- display(HTML("<style>.container { width:90% !important; }</style>"))
-
-
- from plotly.subplots import make_subplots
- from scipy.signal import peak_widths
- from sklearn import metrics
- import plotly.offline as pyoff
-
-
- half_1 = peak_widths(self._obs_curve_1, self._obs_peaks_1, rel_height=0.5)
- half_2 = peak_widths(self._obs_curve_2, self._obs_peaks_2, rel_height=0.5)
- fwhm_1 = self._time[int(half_1[3])]-self._time[int(half_1[2])]
- fwhm_2 = self._time[int(half_2[3])]-self._time[int(half_2[2])]
-
-
- fig = make_subplots(rows=2, cols=2,vertical_spacing=0.15,
- subplot_titles=("{} concentration".format(self._observable), "Amplitude", "Area under the curve", "Full Width at Half Maximum"))
-
- ####################
- #### MAIN PLOT ####
- ####################
- fig.add_trace(go.Scatter(x=self._time, y=self._obs_curve_1*1E3, name='control'), row=1, col=1)
- fig.add_trace(go.Scatter(x=self._time, y=self._obs_curve_2*1E3, name='{}-fold'.format(self._fold)), row=1, col=1)
- fig.add_trace(go.Scatter(x=self._time[self._obs_peaks_1], y=self._obs_curve_1[self._obs_peaks_1]*1E3,
- name='max value', showlegend=False, mode='markers',
- marker=dict(symbol='x', size=13, color='Black')), row=1,col=1)
- fig.add_trace(go.Scatter(x=self._time[self._obs_peaks_2], y=self._obs_curve_2[self._obs_peaks_2]*1E3,
- name='max value', showlegend=False, mode='markers',
- marker=dict(symbol='x', size=13, color='Black')), row=1,col=1)
- fig.add_shape(type='line', x0=self._time[int(half_1[2])],y0=half_1[1][0]*1E3, x1=self._time[int(half_1[3])], y1=half_1[1][0]*1E3,
- line=dict(color='Blue',dash='dash'),xref='x',yref='y', row=1, col=1)
- fig.add_shape(type='line', x0=self._time[int(half_2[2])],y0=half_2[1][0]*1E3, x1=self._time[int(half_2[3])], y1=half_2[1][0]*1E3,
- line=dict(color='Red',dash='dash'),xref='x',yref='y', row=1, col=1)
-
- # Update xaxis properties
- fig.update_xaxes(title_text="Time (s)", showgrid=False, row=1, col=1, titlefont=dict(size=18),
- linecolor='black', linewidth=2,
- ticks='inside', tickfont=dict(size=18), tickcolor='black', ticklen=10, tickwidth=2)
-
- fig.update_yaxes(title_text=self._observable+' (nM)', titlefont=dict(size=18), showgrid=False, row=1, col=1,
- linecolor='black', linewidth=2,
- ticks='inside', tickfont=dict(size=18), tickcolor='black', ticklen=10, tickwidth=2)
-
-
- ####################
- #### AMPLITUDE ####
- ####################
-
- AMP_labels = [1,2]
- AMP_values = [self._vmax_obs_curve_1, self._vmax_obs_curve_2]
- fig.add_trace(go.Bar(x=AMP_labels,y=AMP_values, width = [0.35,0.35], showlegend=False, marker_color='black', name=''), row=1, col=2 )
-
-
-
- # Update xaxis properties
- fig.update_xaxes(row=1, col=2, showgrid=False, linecolor='black', linewidth=2, range=[0,3],
- tickmode='array', tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
-
- fig.update_yaxes(showgrid=False, range=[round((min(AMP_values)-min(AMP_values)*0.5)/5)*5,round((max(AMP_values)+max(AMP_values)*0.5)/5)*5 ], row=1, col=2,
- title_text=self._observable+' (nM)', titlefont=dict(size=18),
- linecolor='black', linewidth=2, ticks='inside', ticklen=10, tickwidth=2, tickfont=dict(size=18))
-
- # Add diff lines
- AMP_diffs = [max(AMP_values) - v for v in AMP_values]
- AMP_diff_labels = dict(zip(AMP_labels, AMP_diffs))
- fig.add_trace(go.Scatter(name='',x=[1,1.5,2], y=[max(AMP_values)+(max(AMP_values)*0.3)]*3, mode = 'lines+text',showlegend=False, line=dict(color='black', width=1),text=['', 'diff. = {} nM'.format(round(AMP_diffs[0], 3)),''], textposition='top center'), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[0]-0.175, AMP_labels[0]+0.175], y=[AMP_values[0]+(AMP_values[0]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[1]-0.175, AMP_labels[1]+0.175], y=[AMP_values[1]+(AMP_values[1]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[0], AMP_labels[0]], y=[AMP_values[0]+(AMP_values[0]*0.03), max(AMP_values)+(max(AMP_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[1], AMP_labels[1]], y=[AMP_values[1]+(AMP_values[1]*0.03), max(AMP_values)+(max(AMP_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
-
-
- ####################
- #### AUC ####
- ####################
-
- # Data
- AUC_labels = [1,2]
- AUC_values = [round(metrics.auc(self._time, self._obs_curve_1),2), round(metrics.auc(self._time, self._obs_curve_2),2)]
- fig.add_trace(go.Bar(x=AUC_labels,y=AUC_values, width = [0.35,0.35], showlegend=False, marker_color='black', name=''), row=2, col=1 )
-
- # Update xaxis properties
- fig.update_xaxes(row=2, col=1, tickmode='array', showgrid=False, range=[0,3], linecolor='black', linewidth=2,
- tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
-
- fig.update_yaxes(row=2, col=1,showgrid=False, title_text=self._observable+' (nM)', range=[round((min(AUC_values)-min(AUC_values)*0.5)/5)*5,round((max(AUC_values)+max(AUC_values)*0.5)/5)*5],
- titlefont=dict(size=18),linecolor='black', linewidth=2,
- ticks='inside', tickfont=dict(size=18),ticklen=10, tickwidth=2)
-
- # Add diff lines
- AUC_diffs = [max(AUC_values) - v for v in AUC_values]
- AUC_diff_labels = dict(zip(AUC_labels, AUC_diffs))
- fig.add_trace(go.Scatter(name='',x=[1,1.5,2], y=[max(AUC_values)+(max(AUC_values)*0.3)]*3, mode = 'lines+text',showlegend=False,
- line=dict(color='black', width=1), text=['', 'diff. = {} nM'.format(round(AUC_diffs[0], 3)),''], textposition='top center'), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[0]-0.175, AUC_labels[0]+0.175], y=[AUC_values[0]+(AUC_values[0]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[1]-0.175, AUC_labels[1]+0.175], y=[AUC_values[1]+(AUC_values[1]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[0], AUC_labels[0]], y=[AUC_values[0]+(AUC_values[0]*0.03), max(AUC_values)+(max(AUC_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[1], AUC_labels[1]], y=[AUC_values[1]+(AUC_values[1]*0.03), max(AUC_values)+(max(AUC_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
-
-
- ####################
- #### FWHM ####
- ####################
- # Data
- FWHM_labels = [1,2]
- FWHM_values = [fwhm_1, fwhm_2]
- fig.add_trace(go.Bar(x=FWHM_labels,y=FWHM_values, width = [0.35,0.35], showlegend=False,marker_color='black', name=''), row=2, col=2 )
-
- # Update xaxis properties
- fig.update_xaxes(row=2, col=2, showgrid=False, range=[0,3], linecolor='black', linewidth=2,
- tickmode='array', tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
-
- fig.update_yaxes(row=2, col=2, showgrid=False, range=[self.pathway_parameters['time_in'],round((max(FWHM_values)+(max(FWHM_values)-self.pathway_parameters['time_in'])*0.5)/5)*5],
- title_text='Time (s)', titlefont=dict(size=18), linecolor='black', linewidth=2,
- ticks='inside', ticklen=10, tickwidth=2, tickfont=dict(size=18))
-
- # Add diff lines
- FWHM_diffs = [max(FWHM_values) - v for v in FWHM_values]
- FWHM_diff_labels = dict(zip(FWHM_labels, FWHM_diffs))
- line_height = max(FWHM_values)+((max(FWHM_values)-self.pathway_parameters['time_in'])*0.30)
- fig.add_trace(go.Scatter(x=[1,1.5,2], y=[line_height]*3, mode = 'lines+text',showlegend=False, line=dict(color='black', width=1),name='',
- text=['', 'diff. = {} s'.format(round(FWHM_diffs[0], 3)),''], textposition='top center'), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[0]-0.175, FWHM_labels[0]+0.175], y=[FWHM_values[0]+(FWHM_values[0]*0.005)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[1]-0.175, FWHM_labels[1]+0.175], y=[FWHM_values[1]+(FWHM_values[1]*0.005)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[0], FWHM_labels[0]], y=[FWHM_values[0]+(FWHM_values[0]*0.005), line_height], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[1], FWHM_labels[1]], y=[FWHM_values[1]+(FWHM_values[1]*0.005), line_height], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
-
-
- ####################
- #### FIGURE ####
- ####################
-
- fig.update_layout(height=1200, width=1300, title_text="", plot_bgcolor='white',showlegend=True,
- legend=dict(yanchor="top", x=0.3, y=.99,font=dict(family="sans-serif", size=14,color="black")))
- fig.update_annotations(font_size=20, font_color='black')
-
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
-
- return
diff --git a/ssbtoolkit/__init__py b/ssbtoolkit/__init__py
new file mode 100644
index 0000000..73756ce
--- /dev/null
+++ b/ssbtoolkit/__init__py
@@ -0,0 +1,15 @@
+import pkgutil
+
+__all__ = []
+for loader, module_name, is_pkg in pkgutil.walk_packages(__path__):
+ __all__.append(module_name)
+ _module = loader.find_module(module_name).load_module(module_name)
+ globals()[module_name] = _module
+
+
+import sys, os, warnings
+sys.path.insert(0, os.path.abspath(os.path.split(os.path.realpath(__file__))[0]))
+warnings.simplefilter(action='ignore')
+
+
+
diff --git a/ssbtoolkit/__pycache__/Binding.cpython-39.pyc b/ssbtoolkit/__pycache__/Binding.cpython-39.pyc
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diff --git a/ssbtoolkit/__pycache__/Utils.cpython-39.pyc b/ssbtoolkit/__pycache__/Utils.cpython-39.pyc
index a8b0c57ab461229eea031894352efa76c5bb7d7b..ff1259fb1d1bac67343b743163f5fa046c7adeeb 100644
GIT binary patch
delta 2936
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diff --git a/ssbtoolkit/__pycache__/__init__.cpython-39.pyc b/ssbtoolkit/__pycache__/__init__.cpython-39.pyc
index a650def0a4ad5939e819b758c2da741abc190047..48ab15b99374e7ef7ef007a6d7495fb46a4bf955 100644
GIT binary patch
delta 7412
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C3W>1*
delta 7096
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diff --git a/ssbtoolkit/pathways/__pycache__/Gi.cpython-39.pyc b/ssbtoolkit/pathways/__pycache__/Gi.cpython-39.pyc
index 90575c1b81be1350a768a8c3e93401a12ac20d7d..465eaabb494545925492e3a595ed849104b0d3f8 100644
GIT binary patch
delta 282
zcmX@$zQ>(Ak(ZZ?0SIyz)@<Z9kYKc#Y$KsGIY}aiF>3O8AelPZM$()u9jKxxV{)aW
zGg}deSv>iWWD;ZiWK}6;#{9{iQt})XKw(YBqVmZFQW=bin{P@@V`8kFTp%mOST(s@
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enk+?)AmJtu(E=ixC+}0zWNQU6Cr^H>bQu7kBSo12
delta 333
zcmdnve!!hOk(ZZ?0SFGHR&V4skYIF}Y$Kt>qMMRhHaSirhw;|r(?Bw9vW28MTLw^B
zQRd`QNoTfV5VK_RKFK7;gvp9h%8Uh*U8UqXDuKe9j71fbbEGmDlQv(Kn#ROfH91FC
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z9z-aBh`h-x3QHIZC$Cg+6{!IU)PV@ND{pOPQe4O=J{2ga$x_q=l4}MLtstUh@-8J!
Nwl)xR>g1P7mjU|?Q#Swr
diff --git a/ssbtoolkit/pathways/__pycache__/Gq.cpython-39.pyc b/ssbtoolkit/pathways/__pycache__/Gq.cpython-39.pyc
index fc9fdfb33151758ee35559f7b552a6788f835e08..67c3f6b11c0ab685fa225bc8fa8057291a9f91dc 100644
GIT binary patch
delta 31
lcmdmNvfhL{k(ZZ?0SIyz)@<aq5MZ>K>>{AFIZNOqCjfp`2rd8s
delta 36
qcmZ2)ve|??k(ZZ?0SLCIR&V6C5MXqe>>{AVqMMRhwmD7UBqso=F$t>x
diff --git a/ssbtoolkit/pathways/__pycache__/Gs.cpython-39.pyc b/ssbtoolkit/pathways/__pycache__/Gs.cpython-39.pyc
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diff --git a/ssbtoolkit/ssbmain.py.txt b/ssbtoolkit/ssbmain.py.txt
deleted file mode 100644
index 9031e98..0000000
--- a/ssbtoolkit/ssbmain.py.txt
+++ /dev/null
@@ -1,1477 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
-# Copyright 2021 Rui Ribeiro
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-__author__ = "Rui Ribeiro"
-__email__ = "rui.ribeiro@univr.it"
-
-#system libraries
-from re import sub
-import sys
-import os
-import importlib
-import urllib.request as urllib
-from glob import glob
-
-#Scientific Libraries
-#import math
-import numpy as np
-import pandas as pd
-from pandas.core import construction
-from scipy.optimize import curve_fit, minimize
-from sklearn.preprocessing import minmax_scale
-
-#Plotting Libraries
-import plotly.graph_objs as go
-import pylab as pl
-from matplotlib import *
-
-#PYSB
-from pysb import *
-from pysb.macros import *
-from pysb.simulator import ScipyOdeSimulator
-
-#Layout libraries
-#import qgrid
-
-#directories (problem with sphinx)
-abs_path=(os.path.join(os.path.split(os.getcwd())[0], 'src/lib'))
-sys.path.insert(0, os.path.abspath(abs_path))
-try:
- from ssbtoolkit.directories import *
- import ssbtoolkit.utils as utils
-except:
- from directories import *
- from utils import *
-
-
-import warnings
-warnings.simplefilter(action='ignore')
-
-
-
-
-#Human Target Receptors DataBase directory PATH
-HuTRdb_path = os.path.join(os.getcwd(),'SSBtoolkit/src/databases/HuTRdb.sqlite3')
-
-"""
-SSBtoolkit API
-"""
-
-class binding:
- """This class simulate ligand-target binding curves."""
- def __init__(self):
- self.receptor_conc = None
- self.lig_conc_range = None
- self.pKd = None
- self.submax_concentration = None
-
- def bind(self, **kwargs):
- """
- Applies an function to calculate the fraction of occupited receptors at equilibrium.
-
- :parameter receptor_conc: Required (kwarg flt): concentration of receptor
- :parameter lig_conc_range: Required (kwarg array): array of range of ligand concentration
- :parameter pKd: Required (kwarg flt): pKd value of the ligand
- """
-
- if 'receptor_conc' not in kwargs: raise TypeError("ERROR: receptor_conc is missing")
- if 'lig_conc_range' not in kwargs: raise TypeError("ERROR: lig_conc_range is missing")
- if 'pKd' not in kwargs: raise TypeError("ERROR: pKd is missing")
-
- self._receptor_conc = kwargs.pop('receptor_conc')
- self._lig_conc_range = kwargs.pop('lig_conc_range')
- self._pKd = kwargs.pop('pKd')
-
- binding_data=[]
- for conc in self._lig_conc_range:
- binding_data.append(utils.LR_eq_conc(self._receptor_conc, conc, 0, self._pKd, 0))
- self.binding_data=binding_data
- return self.binding_data
-
- def maxbend(self):
- """
- Calculates the maximum bending point of a sigmoid-shaped curve according to the mathod of Sebaugh et al., 2003.
-
- :parameter drug_receptor: Required (int): concentration of the receptor
- :parameter lig_conc_range: Required (array): array of a range of ligand concentration
- :return: instance .submax_concentration (flt)
-
- .. note:: The minimization uses the Nelder-Mead method.
- """
-
- from scipy.optimize import curve_fit, minimize
-
-
- def sigmoid(X, Bottom, Top, Kd, p):
- return Bottom + (Top-Bottom)/(1+np.power((Kd/X),p))
-
- xfit = np.geomspace(np.min(self._lig_conc_range), np.max(self._lig_conc_range), 50000) #warning: shoud this be the minimum and maximum of concentration
- popt, pcov = curve_fit(sigmoid, self._lig_conc_range, self.binding_data, bounds=([np.min(self.binding_data),-np.inf,-np.inf, 0.5],[np.inf,np.max(self.binding_data),np.inf, 2.5]))
-
-
- def sigmoid_deriv_b(x, a,d,c,b):
- return (x/c)**b*(a - d)*np.log(x/c)/((x/c)**b + 1)**2
-
- min_value = minimize(sigmoid_deriv_b, np.max(xfit), args=(popt[0],popt[1],popt[2],popt[3]), method = 'Nelder-Mead')
-
- self.submax_concentration = round(min_value.x[0],3)
- return self.submax_concentration
-
- def show_curve(self):
- """
- Plots ligand-target binding curve
- """
-
- #import plotly
- import plotly.graph_objs as go
- from scipy.optimize import curve_fit
- from sklearn.preprocessing import minmax_scale
-
- ##Fitting curve to the data
-
- yy = minmax_scale(self.binding_data)*100
-
- def equation_dose(X, Bottom, Top, EC50, p):
- return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
-
- popt, pcov = curve_fit(equation_dose, self._lig_conc_range, yy, bounds=([np.min(yy),-np.inf,-np.inf, 0.5],[np.inf,np.max(yy),np.inf, 2.5]))
-
- xfit = np.geomspace(np.min(self._lig_conc_range), np.max(self._lig_conc_range), 50000) # These values are the same as the values for the simulation time and not ligand concentration
- yfit = minmax_scale(equation_dose(xfit, *popt))*100
-
-
-
- trace1 = go.Line(x=xfit, y=yfit, showlegend=False, name='radioligand')
- if self.submax_concentration:
- xsubmaximal = np.array(self.submax_concentration)
- ysubmaximal = np.array(equation_dose(xsubmaximal, *popt))
- trace2 = go.Scatter(x=xsubmaximal, y=ysubmaximal, showlegend=True, mode='markers', name='submaximal ({} μM)'.format(xsubmaximal),
- marker=dict(size=14))
- else: trace2=[]
-
-
- layout = dict(title = '',
- xaxis = dict(
- title = '[ligand] μM',
- type ='log',
- exponentformat='e',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '% occupied receptors',
-
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650,
- )
- if self.submax_concentration:
- fig = go.Figure(data=[trace1, trace2], layout=layout)
- else:
- fig = go.Figure(data=[trace1], layout=layout)
- return fig
-
-class simulation:
- """
- This class simulates the mathematical models of the signaling pathways.
- """
- class activation:
- """
- Simulation of the activation of signaling pathways (i.e. activation by agonists)
- """
- def __init__(self):
- self._ligands=None
- self._affinities=None
- self._pathway=None
- self._receptor_conc=None
- self._lig_conc_range=None
- self._ttotal=None
- self._nsteps=None
- self._binding_kinetics=True
- self._binding_kinetic_parameters=None
- self.simulation_data=None
- self.processed_data=None
-
- def SetSimulationParameters(self, **kwargs):
- """
- :parameter ligands: Required (kwargs list): list of ligands' names (str)
- :parameter affinities: Required (kwargs list): list of pKd values (flt)
- :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
- :parameter receptor_conc: Required (kwargs flt): receptors concentration (nM)
- :parameter lig_conc_range: Required (kwargs array): range of ligands' concentration
- :parameter ttotal: Required (kwargs int): simulation time (seconds)
- :parameter nsteps: Required (kwargs int): simulation time step
- :parameter binding_kinetics: Optional (kwargs boolean): default (False)
-
-
- .. warning:: the order of the lists of ligands names and affinities list must be the same.
-
- """
- self._ligands= kwargs.pop('ligands')
- if 'affinities' in kwargs:
- self._affinities=kwargs.pop('affinities')
- self._pathway=kwargs.pop('pathway')
- self._receptor_conc=kwargs.pop('receptor_conc')
- self._lig_conc_range=kwargs.pop('lig_conc_range')
- self._ttotal=kwargs.pop('ttotal')
- self._nsteps=kwargs.pop('nsteps')
- self._binding_kinetics=kwargs.pop('binding_kinetics')
- if 'binding_kinetic_parameters' in kwargs:self._binding_kinetic_parameters=kwargs.pop('binding_kinetic_parameters')
- self._DefaultPathwayParametersDataFrame=pd.DataFrame()
-
- return
-
- def PathwayParameters(self):
- """
- Display table with default pathway parameters.
-
- .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
-
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def UserPathwayParameters(self, path):
- """
- Import user pathway parameters.
-
- :parameter path: Required (kwarg str): directory path
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def PathwayParametersToCSV(self, path):
- """
- Export pathway parameters into CSV format.
-
- :parameter path: Required (kwarg str): directory path
- """
- self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
- print('saved in:', path)
- return
-
- def Reactions(self):
- """
- Display pathway reactions.
- """
- from IPython.display import display, HTML
- display(HTML("<style>.container {width:90% !important}</style>"))
- return pd.read_csv('src/lib/pathways/{}_reactions.csv'.format(self._pathway))
-
- def Run(self):
- '''
- This function runs the pathway simulation and returns the raw simulation data.
- '''
-
- #Check inputs
- if self._ligands==None: raise TypeError("ligands list undefined.")
- elif self._pathway==None: raise TypeError("pathway name undefined.")
- elif self._binding_kinetics==False and self._affinities==None: raise TypeError("affinity_values_dict undefined.")
- elif self._binding_kinetics==True and self._affinities==None: pass
- elif self._lig_conc_range.any() == False: raise TypeError("lig_conc_range undefined.")
- elif self._ttotal==None: raise TypeError("ttotal undefined.")
- elif self._nsteps==None: raise TypeError("nsteps undefined.")
- elif self._receptor_conc==None: raise TypeError("receptor_conc undefined.")
- else: pass
-
-
- #Check Pathway availability and import it
- available_pathways = ['Gs', 'Gi', 'Gq']
- if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
- if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Pathways available: "Gs", "Gi", "Gq".')
- mypathway = importlib.import_module('.'+self._pathway, package='src.lib.pathways')
-
- #Get default pathway parameters
- if self._DefaultPathwayParametersDataFrame.empty and self._binding_kinetic_parameters==None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self._binding_kinetic_parameters is None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
- except:
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty and self._binding_kinetic_parameters is not None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self._binding_kinetic_parameters is not None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = {**newparameters.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters}
- except:
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters}
-
- #Input
- t = pl.geomspace(0.00001, self._ttotal, num=self._nsteps) # (a,b,c); a is the starting time ; b is the total time simulated ; c is the number of points
-
-
- #Output
- simulation_data={}
-
- #Function
- for ligand in self._ligands:
- ligand_name = os.path.splitext(str(ligand))[0]
- data=[]
- utils.printProgressBar(0, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
- ###DANGER ZONE###
- if self._binding_kinetic_parameters is not None:
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self._binding_kinetic_parameters[self._ligands.index(ligand)]}
- ######################
-
- for idx in range(len(self._lig_conc_range)):
-
- ligand_conc = self._lig_conc_range[idx]
- if self._binding_kinetics == False:
- #get LR conc
- parameters = {**self._PathwayParameters, 'R_init':self._receptor_conc}
- LR_conc_init = utils.LR_eq_conc(self._receptor_conc, ligand_conc, 0, self._affinities[self._ligands.index(ligand)], 0)
- mymodel = mypathway.network(LR=LR_conc_init, kinetics=False, **parameters)
- simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
- yout = simres.all
-
- elif self._binding_kinetics == True:
- parameters={**self._PathwayParameters,'R_init':self._receptor_conc, 'L_init':self._lig_conc_range[idx] }
- mymodel = mypathway.network(kinetics=True, **parameters)
- simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
- yout = simres.all
-
- d1={'ligand_conc':ligand_conc, 'time':t }
-
- for idx2 in range(len(mypathway.list_of_observables)):
- d2={mypathway.list_of_observables[idx2]:yout[mypathway.list_of_observables[idx2]]}
- d1.update(d2)
- data.append(d1)
- utils.printProgressBar(idx + 1, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
-
- simulation_data[ligand_name] = {'sim_data':data,
- 'label':ligand_name,}
- self.simulation_data = simulation_data
- return
-
- def Analysis(self):
- '''
- This function calculates the dose-response effect.
-
- :return: instance of processed_data
- '''
-
- if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.activation.run() must be run first.')
-
- from sklearn.preprocessing import minmax_scale
-
- # Define all the lists and dictionaries used in this function
- raw_data=[]
- normalized_data=[]
- fitted_data=[]
-
- dose={}
-
- #defining concentration range
- lig_conc_min = self._lig_conc_range.min()
- lig_conc_max = self._lig_conc_range.max()
-
- #Main function
- for ligand in self.simulation_data:
-
- #definig and dictionaries used in this loop:
- raw_data_dict={}
- normalized_data_dict={}
- fitted_data_dict={}
-
- # Calculate dose-response curve
- #get metabolite concentration, rescale, and transform data if pathway/metabolite decrease
- # metabolite_raw is not normalized
- if self._pathway == 'Gi' or self._pathway == 'Gz(Gi)':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(1-np.array(metabolite_conc_raw))
- elif self._pathway == 'Gs':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- elif self._pathway == 'Gq':
- metabolite='IP3'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- else: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
-
-
- ## save results
- raw_data_dict['x']=self._lig_conc_range
- raw_data_dict['y']=metabolite_conc_raw
- raw_data_dict['label']=self.simulation_data[ligand]['label']
-
- normalized_data_dict['x']=self._lig_conc_range
- normalized_data_dict['y']=metabolite_conc_norm
- normalized_data_dict['label']=self.simulation_data[ligand]['label']
-
- ## create a list of all data
- raw_data.append(raw_data_dict)
- normalized_data.append(normalized_data_dict)
-
- ##Fitting curve to the data
-
- def equation_dose(X, Bottom, Top, EC50, p):
- return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
-
- popt_EC50, pcov = curve_fit(equation_dose, self._lig_conc_range, metabolite_conc_norm, bounds=([np.min(metabolite_conc_norm),-np.inf,-np.inf, 0.5],[np.inf,np.max(metabolite_conc_norm),np.inf, 2.5]))
-
- xfit_EC50 = np.geomspace(lig_conc_min, lig_conc_max, 50000) # These values are the same as the values for the simulation time and not ligand concentration
- yfit_EC50 = equation_dose(xfit_EC50, *popt_EC50)
-
- fit_EC50={'x':xfit_EC50, 'y':yfit_EC50, 'label':self.simulation_data[ligand]['label']}
-
- dose[ligand] = {'raw_data': raw_data_dict,
- 'normalized_data':normalized_data_dict ,
- 'fitted_data': fit_EC50,
- 'EC50 (μM)': round(popt_EC50[2],5),
- 'pEC50': round(-np.log10(popt_EC50[2]*1E-6),2)}
-
- self.processed_data=dose
- return
-
- def Curve(self, save=False, filename=None):
- '''
- Plots the dose-response curve.
- '''
-
- if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.activation.run() must be run first.')
-
- import plotly
- import plotly.graph_objs as go
- import plotly.offline as pyoff
-
- colors = plotly.colors.DEFAULT_PLOTLY_COLORS
-
- plot_data=[]
-
- color_id=0
- for ligand in self.processed_data:
- trace_norm = go.Scatter(x=self.processed_data[ligand]['normalized_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['normalized_data']['y'])*100 ,
- mode='markers',
- showlegend=True,
- name=self.processed_data[ligand]['normalized_data']['label'],
- marker=dict(color=colors[color_id]))
- plot_data.append(trace_norm)
-
- trace_fitted = go.Scatter(x=self.processed_data[ligand]['fitted_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['fitted_data']['y'])*100,
- mode='lines',
- showlegend=False,
- name=self.processed_data[ligand]['fitted_data']['label'],
- line=dict(color=colors[color_id]))
- plot_data.append(trace_fitted)
- color_id +=1
-
- layout = dict(title = '',
- xaxis = dict(
- title = '[ligand] μM',
- type ='log',
- #range = [-3, 2],
- exponentformat='e',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '% Response',
- #range = [0, 100],
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650,
- )
-
- fig = go.Figure(data=plot_data, layout=layout)
- #fig['layout']['yaxis'].update(autorange = True)
-
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
- return
-
- def Potency(self):
- '''
- Return the potency values as a pandas DataFrame.
- '''
- import pandas as pd
- data = simulation.activation.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- return df
-
- def PotencyToDict(self):
- '''
- Convert potencies into a dictionary.
- '''
-
- #dependencies
- if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.activation.analysis() must be run first.')
-
- kvalues={}
- for ligand in self.processed_data:
- IC50 = list(self.processed_data[ligand].keys())[-2]
- IC50_value = self.processed_data[ligand][IC50]
- pIC50 = list(self.processed_data[ligand].keys())[-1]
- pIC50_value = self.processed_data[ligand][pIC50]
- kvalues[ligand]={IC50:IC50_value, pIC50:pIC50_value}
- return kvalues
-
- def PotencyToCSV(self, path):
- '''
- Exports the potency values into csv format.
-
- :parameter path: Required (kwarg str): directory path to save the csv file
- '''
-
- data = simulation.activation.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- df.to_csv(path, index=False)
- return
-
- class inhibition:
- """
- Simulation of the inhibition of signaling pathways (i.e. inhibition by antagonists).
- """
- def __init__(self):
- self._agonist=None
- self._agonist_affinity=None
- self._agonist_submaximal_conc=None
- self._antagonists=None
- self._antagonists_affinities=None
- self._pathway=None
- self._receptor_conc=None
- self._lig_conc_range=None
- self._ttotal=None
- self._nsteps=None
- self._binding_kinetics=False
- self._binding_kinetic_parameters=None
- self.simulation_data=None
- self.processed_data=None
-
- def SetSimulationParameters(self, **kwargs):
- """
- :parameter agonist: Required (kwargs str): agonist name
- :parameter agonist_affinity: Required (kwargs flt): agonist pKd value
- :parameter agonist_submaximal_conc: Required (kwargs flt): agonist submaximal concentration
- :parameter antagonists: Required (kwargs list):list of antagonists names (str)
- :parameter antagonists_affinities: Required (kwargs list): list of antagonists affinity values (flt)
- :parameter antagonists_conc_range: Required (kwargs array): range of ligands' concentration (nM)
- :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
- :parameter receptor_conc: Required (kwargs flt): receptors concentration (nM)
- :parameter ttotal: Required (kwargs int): simulation time (seconds)
- :parameter nsteps: Required (kwargs int): simulation time step
- :parameter kinetics: Optional (kwargs boolean): default (False)
-
-
- :return: instances of all parameters
-
- .. warning:: the order of the lists of the antagonists names and affinities list must be the same.
-
- """
- self._agonist= kwargs.pop('agonist')
- self._agonist_affinity=kwargs.pop('agonist_affinity')
- self._agonist_submaximal_conc=kwargs.pop('agonist_submaximal_conc')
- self._antagonists=kwargs.pop('antagonists')
- self._antagonists_affinities=kwargs.pop('antagonists_affinities')
- self._pathway=kwargs.pop('pathway')
- self._receptor_conc=kwargs.pop('receptor_conc')
- self._lig_conc_range=kwargs.pop('lig_conc_range')
- self._ttotal=kwargs.pop('ttotal')
- self._nsteps=kwargs.pop('nsteps')
- if 'kinetics' in kwargs:
- self._binding_kinetics=kwargs.pop('kinetics')
- if self._binding_kinetics==True: raise TypeError("The of Kinetic parameters during an inhibition simulation it is not supported yet.")
- else: self._binding_kinetics=False
- if 'binding_kinetic_parameters' in kwargs:
- self._binding_kinetic_parameters=kwargs.pop('binding_kinetic_parameters')
- self._DefaultPathwayParametersDataFrame=pd.DataFrame()
-
- return
-
- def PathwayParameters(self):
- """
- Display table with default pathway parameters.
-
- .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
-
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def UserPathwayParameters(self, path):
- """
- Import user pathway parameters.
-
- :parameter path: Required (kwarg str): directory path
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def PathwayParametersToCSV(self, path):
- """
- Export pathway parameters into CSV format.
-
- :parameter path: Required (kwarg str): directory path
- """
- self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
- print('saved in:', path)
- return
-
- def Reactions(self):
- """
- Display pathway reactions.
- """
- from IPython.display import display, HTML
- display(HTML("<style>.container {width:90% !important}</style>"))
- return pd.read_csv('src/lib/pathways/{}_reactions.csv'.format(self._pathway))
-
- def Run(self):
- '''
- This function runs the pathway simulation and returns the raw simulation data.
- '''
-
- #Check inputs
- if self._agonist==None: raise TypeError("agonist undefined.")
- elif self._agonist_affinity==None: raise TypeError("agonist_affinity undifined.")
- elif self._antagonists==None: raise TypeError("antagonists list undefined.")
- elif self._antagonists_affinities==None: raise TypeError("antagonists affinity values undefined.")
- elif self._pathway==None: raise TypeError("pathway undefined.")
- elif self._lig_conc_range.any() == False: raise TypeError("lig_conc_range undefined.")
- elif self._agonist_submaximal_conc == None: raise TypeError("agonist_submaximal_conc undifined.")
- elif self._ttotal==None: raise TypeError("ttotal undefined.")
- elif self._nsteps==None: raise TypeError("nsteps undefined.")
- elif self._receptor_conc==None: raise TypeError("receptor_conc undefined.")
- elif self._binding_kinetics==True: raise TypeError("The of Kinetic parameters during an inhibition simulation it is not supported yet.")
- else: pass
-
- #check pathway
- available_pathways = ['Gs', 'Gi', 'Gq']
- if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
- if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
- mypathway = importlib.import_module('.'+self._pathway, package='src.lib.pathways')
-
- #Get default pathway parameters
- if self._DefaultPathwayParametersDataFrame.empty:
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty is False:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
- except:
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- #Input
- t = pl.geomspace(0.00001, self._ttotal, num=self._nsteps) # (a,b,c); a is the starting time ; b is the total time simulated ; c is the number of points
-
- #Output
- simulation_data={}
-
- #Function
- for ligand in self._antagonists:
- ligand_name = os.path.splitext(ligand)[0]
- data=[]
- utils.printProgressBar(0, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
- for idx in range(len(self._lig_conc_range)):
-
- ligand_conc = self._lig_conc_range[idx]
-
- #get LR conc
- parameters = {**self._PathwayParameters, 'R_init':self._receptor_conc}
- LR_conc_init = utils.LR_eq_conc(self._receptor_conc, self._agonist_submaximal_conc, ligand_conc, self._agonist_affinity, self._antagonists_affinities[self._antagonists.index(ligand)])
- mymodel = mypathway.network(LR=LR_conc_init, kinetics=False, **parameters)
- simres = ScipyOdeSimulator(mymodel, tspan=t, compiler='cython').run()
- yout = simres.all
-
- d1={'ligand_conc':ligand_conc, 'time':t }
-
- for idx2 in range(len(mypathway.list_of_observables)):
- d2={mypathway.list_of_observables[idx2]:yout[mypathway.list_of_observables[idx2]]}
- d1.update(d2)
- data.append(d1)
- utils.printProgressBar(idx + 1, len(self._lig_conc_range), prefix = "{:<15}".format(ligand_name[:15]), suffix = 'Complete', length = 50)
-
- simulation_data[ligand_name] = {'sim_data':data,
- 'label':self._agonist+' + ' + ligand_name}
-
- self.simulation_data=simulation_data
- return
-
- def Analysis(self):
- '''
- This function calculates the dose-response effect.
-
- :return: instance processed_data
- '''
- #dependencies
- if self.simulation_data == None: raise TypeError('There is no simulation data. simulation.inhibition.run() must be run first.')
-
-
- from sklearn.preprocessing import minmax_scale
-
- # Define all the lists and dictionaries used in this function
- raw_data=[]
- normalized_data=[]
- fitted_data=[]
-
- dose={}
-
- #defining concentration range
- #defining concentration range
- lig_conc_min = self._lig_conc_range.min()
- lig_conc_max = self._lig_conc_range.max()
-
- #Main function
- for ligand in self.simulation_data:
-
- #definig and dictionaries used in this loop:
- raw_data_dict={}
- normalized_data_dict={}
- fitted_data_dict={}
-
- # Calculate dose-response curve
- #get metabolite concentration, rescale, and transform data if pathway/metabolite decrease
- # metabolite_raw is not normalized
- if self._pathway == 'Gi' or self._pathway == 'Gz(Gi)':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(1-np.array(metabolite_conc_raw))
- elif self._pathway == 'Gs':
- metabolite='cAMP'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- elif self._pathway == 'Gq':
- metabolite='IP3'
- metabolite_conc_raw=[]
- for i in range(len(self._lig_conc_range)):
- n=np.amax(self.simulation_data[ligand]['sim_data'][i]['obs_'+metabolite]) #cad
- metabolite_conc_raw.append(n)
- metabolite_conc_norm = minmax_scale(np.array(metabolite_conc_raw))
- else: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
-
-
- ## save results
- raw_data_dict['x']=self._lig_conc_range
- raw_data_dict['y']=metabolite_conc_raw
- raw_data_dict['label']=self.simulation_data[ligand]['label']
-
- normalized_data_dict['x']=self._lig_conc_range
- normalized_data_dict['y']=metabolite_conc_norm
- normalized_data_dict['label']=self.simulation_data[ligand]['label']
-
- ## create a list of all data
- raw_data.append(raw_data_dict)
- normalized_data.append(normalized_data_dict)
-
- ##Fitting curve to the data
-
- def equation_dose(X, Bottom, Top, EC50, p):
- return Bottom + (Top-Bottom)/(1+np.power((EC50/X),p))
-
- popt_IC50, pcov = curve_fit(equation_dose, self._lig_conc_range, metabolite_conc_norm, bounds=([np.min(metabolite_conc_norm),-np.inf,-np.inf, 0.5],[np.inf,np.max(metabolite_conc_norm),np.inf, 2.5]))
-
- xfit_IC50 = np.geomspace(lig_conc_min, lig_conc_max, 50000) # These values are the same as the values for the simulation time and not ligand concentration
- yfit_IC50 = equation_dose(xfit_IC50, *popt_IC50)
-
- fit_IC50={'x':xfit_IC50, 'y':yfit_IC50, 'label':self.simulation_data[ligand]['label']}
-
-
-
-
- dose[ligand] = {'raw_data': raw_data_dict,
- 'normalized_data':normalized_data_dict ,
- 'fitted_data': fit_IC50,
- 'IC50 (μM)': round(popt_IC50[2],5),
- 'pIC50': round(-np.log10(popt_IC50[2]*1E-6),2)}
-
- self.processed_data=dose
- return
-
- def Curve(self, save=False, filename=None):
- '''
- Plot the dose-response curve.
- '''
- #dependencies
- if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.inhibition.analysis() must be run first.')
-
- import plotly
- import plotly.graph_objs as go
- import plotly.offline as pyoff
-
- colors = plotly.colors.DEFAULT_PLOTLY_COLORS
-
- plot_data=[]
-
- color_id=0
- for ligand in self.processed_data:
- trace_norm = go.Scatter(x=self.processed_data[ligand]['normalized_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['normalized_data']['y'])*100 ,
- mode='markers',
- showlegend=True,
- name=self.processed_data[ligand]['normalized_data']['label'],
- marker=dict(color=colors[color_id]))
- plot_data.append(trace_norm)
-
- trace_fitted = go.Scatter(x=self.processed_data[ligand]['fitted_data']['x'],
- y=minmax_scale(self.processed_data[ligand]['fitted_data']['y'])*100,
- mode='lines',
- showlegend=False,
- name=self.processed_data[ligand]['fitted_data']['label'],
- line=dict(color=colors[color_id]))
- plot_data.append(trace_fitted)
- color_id +=1
-
- layout = dict(title = '',
- xaxis = dict(
- title = '[ligand] μM',
- type ='log',
- #range = [-4, 2],
- exponentformat='e',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '% Response',
- #range = [0, 100],
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650,
- )
-
- fig = go.Figure(data=plot_data, layout=layout)
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
- return
-
- def constants(self):
- '''
- Returns the potency values.
- '''
-
- #dependencies
- if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.activation.analysis() must be run first.')
-
- kvalues={}
- for ligand in self.processed_data:
- IC50 = list(self.processed_data[ligand].keys())[-2]
- IC50_value = self.processed_data[ligand][IC50]
- pIC50 = list(self.processed_data[ligand].keys())[-1]
- pIC50_value = self.processed_data[ligand][pIC50]
- kvalues[ligand]={IC50:IC50_value, pIC50:pIC50_value}
- self.constants = kvalues
- return kvalues
-
- def PotencyToDict(self):
- '''
- Convert potencies into a dictionary.
- '''
- #dependencies
- if self.processed_data == None: raise TypeError('Simulation data unprocessed. simulation.inhibition.analysis() must be run first.')
-
- kvalues={}
- for ligand in self.processed_data:
- IC50 = list(self.processed_data[ligand].keys())[-2]
- IC50_value = self.processed_data[ligand][IC50]
- pIC50 = list(self.processed_data[ligand].keys())[-1]
- pIC50_value = self.processed_data[ligand][pIC50]
- kvalues[ligand]={IC50:IC50_value, pIC50:pIC50_value}
- return kvalues
-
- def Potency(self):
- '''
- Return the potency values as a pandas DataFrame.
- '''
- import pandas as pd
- data = simulation.inhibition.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- return df
-
- def PotencyToCSV(self, path):
- '''
- Exports the potency values into csv format.
-
- :parameter path: Required (kwarg str): directory path to save the csv file
- '''
- data = simulation.inhibition.PotencyToDict(self)
- df = pd.DataFrame.from_dict(data, orient='index')
- df.to_csv(path, index=False)
- return
-
- class fitModel:
- """
- Fit a model to experimental data.
-
- .. note:: This class was developed to reproduce data from a specific experimental setup. Please see tutorial 4 (OXTR pathay). Use carefully!
- """
- def __init__(self):
-
- #fitting parameters
- self._expratio = None
- self._seed = None
- self._maxiter = None
- self._seed_incrementor = None
- self._target_parameter = None
-
- #Pathway parameters
- self._ttotal = None
- self._nsteps = None
- self._pathway = None
- self._observable = None
- self.pathway_parameters = {}
-
- def SetSimulationParameters(self, **kwargs):
- """
- :parameter pathway_parameters: Required (kwargs): dict of pathway parameters
- :parameter pathway: Required (kwargs str): name of the pathway ('Gs', 'Gi', 'Gq')
- :parameter ttotal: Required (kwargs int): simulation time (seconds)
- :parameter nsteps: Required (kwargs int): simulation time step
- :parameter observable: Required (kwargs str): molecular specie to be measured
-
- :return: instances of all parameters
-
- """
-
- if 'pathway_parameters' in kwargs:
- self.pathway_parameters = kwargs.pop('pathway_parameters')
- #print('pathway_parameters YES')
- self._DefaultPathwayParametersDataFrame=pd.DataFrame()
- if 'ttotal' in kwargs:
- self._ttotal = int(kwargs.pop('ttotal'))
- print('ttotal =', self._ttotal)
- else: raise TypeError("ttotal undefined.")
-
- if 'nsteps' in kwargs:
- self._nsteps = int(kwargs.pop('nsteps', 1000))
- print('nsteps =', self._nsteps)
-
- if 'pathway' in kwargs:
- self._pathway = str(kwargs.pop('pathway'))
- print('pathway ->', self._pathway)
- else: raise TypeError("pathway undefined.")
-
- available_pathways = ['Gs', 'Gi', 'Gq', 'OXTR_pathway']
- if self._pathway == 'Gz(Gi)': self._pathway = 'Gi'
- if self._pathway not in available_pathways: raise Exception('Unvailable Pathway. Please, introduce it manually. Networs available: "Gs", "Gi", "Gq".')
-
-
- if'observable' in kwargs:
- self._observable = str(kwargs.pop('observable'))
- print('observable ->', self._observable)
- else: raise TypeError("observable undefined.")
-
- return
-
- def PathwayParameters(self):
- """
- Display table with default pathway parameters.
-
- .. warning:: this functions requires the qgrid library. It doens't work on Google Colab.
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
-
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def UserPathwayParameters(self, path):
- """
- Import user pathway parameters.
-
- :parameter path: Required (kwarg str): directory path
- """
- import qgrid
- self._DefaultPathwayParametersDataFrame = pd.read_csv(path)
- col_opts = { 'editable': False, 'sortable':False}
- col_defs = {'Value': { 'editable': True, 'width': 150 }}
- self._DefaultPathwayParametersTable = qgrid.show_grid(self._DefaultPathwayParametersDataFrame, column_options=col_opts,column_definitions=col_defs)
- return self._DefaultPathwayParametersTable
-
- def PathwayParametersToCSV(self, path):
- """
- Export pathway parameters into CSV format.
-
- :parameter path: Required (kwarg str): directory path
- """
- self._DefaultPathwayParametersTable.get_changed_df().to_csv(path, index=False)
- print('saved in:', path)
- return
-
- def Reactions(self):
- """
- Display pathway reactions.
- """
- from IPython.display import display, HTML
- display(HTML("<style>.container {width:90% !important}</style>"))
- return pd.read_csv('src/lib/pathways/{}_reactions.csv'.format(self._pathway))
-
- def Run(self, **kwargs):
- """
- Fits of the model to experimental data.
-
- :parameter expratio: Required (kwargs flt): experimental signalling specie concentration ratio
- :parameter target_parameter: Required (kwargs str):kinetic parameter to me modified
- :parameter maxiter: Required (kwargs int): maximum number of iteration
- :parameter seed: Required (kwargs flt): ramdom seed for scaling the modified parameter
- :parameter seed_incrementor: Required (kwargs flt): seed incrementor (each iteration will increment the seed by this value)
- :parameter seed_decrementor: Required (kwargs flt): seed decrementor (each iteration will decrement the seed by this value)
-
- """
-
- from scipy.signal import find_peaks
- import decimal
- #fitting parameters
- if 'expratio' in kwargs:
- self._expratio = float(kwargs.pop('expratio'))
- print('expratio =', self._expratio)
- else: raise TypeError("exratio undefined.")
-
- if 'seed' in kwargs:
- self._seed = float(kwargs.pop('seed'))
- print('seed =', self._seed)
- else: raise TypeError("seed undefined.")
-
- if 'maxiter' in kwargs:
- self._maxiter = int(kwargs.pop('maxiter', 100))
- print('maxiter =', self._maxiter)
-
- if 'seed_incrementor' in kwargs:
- self._seed_incrementor = float(kwargs.pop('seed_incrementor', 0.1))
- print('seed_incrementor =', self._seed_incrementor)
-
- if 'seed_decrementor' in kwargs:
- self._seed_decrementor = float(kwargs.pop('seed_decrementor', 0.1))
- print('seed_decrementor =', self._seed_decrementor)
-
- if 'target_parameter' in kwargs:
- self._target_parameter = str(kwargs.pop('target_parameter'))
- print('target_parameter ->', self._target_parameter)
- else: raise TypeError("target_parameter undefined.")
-
-
- #Get default pathway parameters
- if self._DefaultPathwayParametersDataFrame.empty and self.pathway_parameters==None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self.pathway_parameters is None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = newparameters.set_index('Parameter').iloc[:,0].to_dict()
- except:
- self._PathwayParameters = self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict()
-
- elif self._DefaultPathwayParametersDataFrame.empty and self.pathway_parameters is not None:
- self._DefaultPathwayParametersDataFrame = pd.read_csv('src/lib/pathways/{}_parameters.csv'.format(self._pathway))
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
-
- elif self._DefaultPathwayParametersDataFrame.empty is False and self.pathway_parameters is not None:
- try:
- #extract data from qgrid
- newparameters = self._DefaultPathwayParametersTable.get_changed_df()
- self._PathwayParameters = {**newparameters.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
- except:
- self._PathwayParameters = {**self._DefaultPathwayParametersDataFrame.set_index('Parameter').iloc[:,0].to_dict(), **self.pathway_parameters}
-
-
-
- #simulation parameters:
- if not self._ttotal:
- raise TypeError("simulation parameters unknown. Set the the simulation parameters first wiht set_simulation_parameters()")
-
- #Main function
- mypathway = importlib.import_module('.'+self._pathway, package='src.lib.pathways')
- self.simtime = pl.geomspace(0.00001, self._ttotal, num=self._nsteps)
-
- #Simulation 1
- pathway_model = mypathway.network(LR=None, kinetics=True, **self._PathwayParameters)
- sim1 = ScipyOdeSimulator(pathway_model, tspan=self.simtime,compiler='cython').run()
- self.simres1 = sim1.all
-
- def calc_ratio(self):
-
-
- #Simulation 2
- sim2 = ScipyOdeSimulator(mypathway.network(**self.new_pathway_parameters), tspan=self.simtime, compiler='cython').run()
- self.simres2 = sim2.all
-
- #analysis
- obs_name = 'obs_'+self._observable
- obs_1 = self.simres1[obs_name]
- obs_2 = self.simres2[obs_name]
-
- if 'time_in' in self.new_pathway_parameters:
- self._time = np.take(self.simtime, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
- obs_curve_1 = np.take(obs_1, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
- obs_curve_2 = np.take(obs_2, np.where(self.simtime > int(self.new_pathway_parameters['time_in'])))[0]
-
- else:
- self._time = np.take(self.simtime, np.where(self.simtime>0))[0]
- obs_curve_1 = np.take(obs_1, np.where(self.simtime > 0))[0]
- obs_curve_2 = np.take(obs_2, np.where(self.simtime > 0))[0]
-
- obs_peaks_1, _ = find_peaks(obs_curve_1)
- obs_peaks_2, _ = find_peaks(obs_curve_2)
-
- vmax_obs_curve_1 = obs_curve_1[obs_peaks_1][-1]*1E3
- vmax_obs_curve_2 = obs_curve_2[obs_peaks_2][-1]*1E3
-
- obs_ratio = round(vmax_obs_curve_2/vmax_obs_curve_1, abs(decimal.Decimal(str(self._expratio).rstrip('0')).as_tuple().exponent))
-
- self._obs_curve_1=obs_curve_1
- self._obs_curve_2=obs_curve_2
- self._obs_peaks_1=obs_peaks_1
- self._obs_peaks_2=obs_peaks_2
- self._vmax_obs_curve_1=vmax_obs_curve_1
- self._vmax_obs_curve_2=vmax_obs_curve_2
-
- return obs_ratio
-
- self._iteration=1
- print('\n')
-
- self._lst_ratio=[]
- self._lst_seed=[]
-
- for idx in range(self._maxiter):
-
- prefix = 'iteration'
- iteration_n = str(self._iteration)
- print(f'\r{prefix} {iteration_n}', end='\r')
-
- self.new_pathway_parameters={**self._PathwayParameters, **{self._target_parameter:mypathway.defaultParameters[self._target_parameter]*self._seed}}
- self.obs_ratio = calc_ratio(self)
-
- if self.obs_ratio == self._expratio:
- self._lst_ratio.append(self.obs_ratio)
- self._lst_seed.append(self._seed)
- self._fold=round(self._seed, abs(decimal.Decimal(str(self._expratio).rstrip('0')).as_tuple().exponent))
- print('\n\nDONE!\n', '\nRatio: '+str(self.obs_ratio), '\nFOLD: '+str(self._fold), '\nNumber of iterations: '+str(self._iteration))
- break
- elif self.obs_ratio < self._expratio:
-
- self._lst_ratio.append(self.obs_ratio)
- self._lst_seed.append(self._seed)
- self._iteration+=1
- self._seed += self._seed_incrementor
-
- else:
- self._lst_ratio.append(self.obs_ratio)
- self._lst_seed.append(self._seed)
-
-
- self._iteration+=1
- self._seed -= self._seed_decrementor
-
- return
-
- def plotIterations(self, save=False, filename=None):
- '''
- Plot iterations.
- '''
- import plotly.offline as pyoff
- #dependencies
- if self._iteration == None: raise TypeError('Simulation data not exist. simulation.fitModel.run() must be run first.')
-
- #import plotly
- import plotly.graph_objs as go
-
- iterations = np.arange(1,self._iteration+1)
-
- trace=dict(type='scatter', x=self._lst_seed, y=self._lst_ratio, mode='markers',
- marker=dict(color= iterations, colorscale='Bluered_r', size=14, colorbar=dict(thickness=20, title='iteration number')))
- #axis_style=dict(zeroline=False, showline=True, mirror=True)
- layout = dict(title = '',
- xaxis = dict(
- title = 'seed',
- titlefont=dict(
- size=20
- ),
- tickfont=dict(
- size=20
- )),
- yaxis = dict(
- title = '['+self._observable+']' + ' ratio',
- titlefont=dict(
- size=20),
- tickfont=dict(
- size=20)
-
- ),
- legend=dict(font=dict(size=15)),
- autosize=False,
- width=850,
- height=650
- )
-
- fig = go.Figure(data=[trace], layout=layout)
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
- return fig
-
- def plotCurves(self, save=False, filename=None):
- '''
- Plot the amount of obeservable in function of time, Amplitude, Area Under the Curve, and Full Width at Half Maximum.
-
- :parameter save: Optional (kwarg boolean): default False
- :parameter filename: Optional (kwarg str)
- '''
-
- from IPython.core.display import display, HTML
- display(HTML("<style>.container { width:90% !important; }</style>"))
-
-
- from plotly.subplots import make_subplots
- from scipy.signal import peak_widths
- from sklearn import metrics
- import plotly.offline as pyoff
-
-
- half_1 = peak_widths(self._obs_curve_1, self._obs_peaks_1, rel_height=0.5)
- half_2 = peak_widths(self._obs_curve_2, self._obs_peaks_2, rel_height=0.5)
- fwhm_1 = self._time[int(half_1[3])]-self._time[int(half_1[2])]
- fwhm_2 = self._time[int(half_2[3])]-self._time[int(half_2[2])]
-
-
- fig = make_subplots(rows=2, cols=2,vertical_spacing=0.15,
- subplot_titles=("{} concentration".format(self._observable), "Amplitude", "Area under the curve", "Full Width at Half Maximum"))
-
- ####################
- #### MAIN PLOT ####
- ####################
- fig.add_trace(go.Scatter(x=self._time, y=self._obs_curve_1*1E3, name='control'), row=1, col=1)
- fig.add_trace(go.Scatter(x=self._time, y=self._obs_curve_2*1E3, name='{}-fold'.format(self._fold)), row=1, col=1)
- fig.add_trace(go.Scatter(x=self._time[self._obs_peaks_1], y=self._obs_curve_1[self._obs_peaks_1]*1E3,
- name='max value', showlegend=False, mode='markers',
- marker=dict(symbol='x', size=13, color='Black')), row=1,col=1)
- fig.add_trace(go.Scatter(x=self._time[self._obs_peaks_2], y=self._obs_curve_2[self._obs_peaks_2]*1E3,
- name='max value', showlegend=False, mode='markers',
- marker=dict(symbol='x', size=13, color='Black')), row=1,col=1)
- fig.add_shape(type='line', x0=self._time[int(half_1[2])],y0=half_1[1][0]*1E3, x1=self._time[int(half_1[3])], y1=half_1[1][0]*1E3,
- line=dict(color='Blue',dash='dash'),xref='x',yref='y', row=1, col=1)
- fig.add_shape(type='line', x0=self._time[int(half_2[2])],y0=half_2[1][0]*1E3, x1=self._time[int(half_2[3])], y1=half_2[1][0]*1E3,
- line=dict(color='Red',dash='dash'),xref='x',yref='y', row=1, col=1)
-
- # Update xaxis properties
- fig.update_xaxes(title_text="Time (s)", showgrid=False, row=1, col=1, titlefont=dict(size=18),
- linecolor='black', linewidth=2,
- ticks='inside', tickfont=dict(size=18), tickcolor='black', ticklen=10, tickwidth=2)
-
- fig.update_yaxes(title_text=self._observable+' (nM)', titlefont=dict(size=18), showgrid=False, row=1, col=1,
- linecolor='black', linewidth=2,
- ticks='inside', tickfont=dict(size=18), tickcolor='black', ticklen=10, tickwidth=2)
-
-
- ####################
- #### AMPLITUDE ####
- ####################
-
- AMP_labels = [1,2]
- AMP_values = [self._vmax_obs_curve_1, self._vmax_obs_curve_2]
- fig.add_trace(go.Bar(x=AMP_labels,y=AMP_values, width = [0.35,0.35], showlegend=False, marker_color='black', name=''), row=1, col=2 )
-
-
-
- # Update xaxis properties
- fig.update_xaxes(row=1, col=2, showgrid=False, linecolor='black', linewidth=2, range=[0,3],
- tickmode='array', tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
-
- fig.update_yaxes(showgrid=False, range=[round((min(AMP_values)-min(AMP_values)*0.5)/5)*5,round((max(AMP_values)+max(AMP_values)*0.5)/5)*5 ], row=1, col=2,
- title_text=self._observable+' (nM)', titlefont=dict(size=18),
- linecolor='black', linewidth=2, ticks='inside', ticklen=10, tickwidth=2, tickfont=dict(size=18))
-
- # Add diff lines
- AMP_diffs = [max(AMP_values) - v for v in AMP_values]
- AMP_diff_labels = dict(zip(AMP_labels, AMP_diffs))
- fig.add_trace(go.Scatter(name='',x=[1,1.5,2], y=[max(AMP_values)+(max(AMP_values)*0.3)]*3, mode = 'lines+text',showlegend=False, line=dict(color='black', width=1),text=['', 'diff. = {} nM'.format(round(AMP_diffs[0], 3)),''], textposition='top center'), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[0]-0.175, AMP_labels[0]+0.175], y=[AMP_values[0]+(AMP_values[0]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[1]-0.175, AMP_labels[1]+0.175], y=[AMP_values[1]+(AMP_values[1]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[0], AMP_labels[0]], y=[AMP_values[0]+(AMP_values[0]*0.03), max(AMP_values)+(max(AMP_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
- fig.add_trace(go.Scatter(name='',x=[AMP_labels[1], AMP_labels[1]], y=[AMP_values[1]+(AMP_values[1]*0.03), max(AMP_values)+(max(AMP_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=1, col=2)
-
-
- ####################
- #### AUC ####
- ####################
-
- # Data
- AUC_labels = [1,2]
- AUC_values = [round(metrics.auc(self._time, self._obs_curve_1),2), round(metrics.auc(self._time, self._obs_curve_2),2)]
- fig.add_trace(go.Bar(x=AUC_labels,y=AUC_values, width = [0.35,0.35], showlegend=False, marker_color='black', name=''), row=2, col=1 )
-
- # Update xaxis properties
- fig.update_xaxes(row=2, col=1, tickmode='array', showgrid=False, range=[0,3], linecolor='black', linewidth=2,
- tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
-
- fig.update_yaxes(row=2, col=1,showgrid=False, title_text=self._observable+' (nM)', range=[round((min(AUC_values)-min(AUC_values)*0.5)/5)*5,round((max(AUC_values)+max(AUC_values)*0.5)/5)*5],
- titlefont=dict(size=18),linecolor='black', linewidth=2,
- ticks='inside', tickfont=dict(size=18),ticklen=10, tickwidth=2)
-
- # Add diff lines
- AUC_diffs = [max(AUC_values) - v for v in AUC_values]
- AUC_diff_labels = dict(zip(AUC_labels, AUC_diffs))
- fig.add_trace(go.Scatter(name='',x=[1,1.5,2], y=[max(AUC_values)+(max(AUC_values)*0.3)]*3, mode = 'lines+text',showlegend=False,
- line=dict(color='black', width=1), text=['', 'diff. = {} nM'.format(round(AUC_diffs[0], 3)),''], textposition='top center'), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[0]-0.175, AUC_labels[0]+0.175], y=[AUC_values[0]+(AUC_values[0]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[1]-0.175, AUC_labels[1]+0.175], y=[AUC_values[1]+(AUC_values[1]*0.03)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[0], AUC_labels[0]], y=[AUC_values[0]+(AUC_values[0]*0.03), max(AUC_values)+(max(AUC_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
- fig.add_trace(go.Scatter(name='',x=[AUC_labels[1], AUC_labels[1]], y=[AUC_values[1]+(AUC_values[1]*0.03), max(AUC_values)+(max(AUC_values)*0.3)], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=1)
-
-
- ####################
- #### FWHM ####
- ####################
- # Data
- FWHM_labels = [1,2]
- FWHM_values = [fwhm_1, fwhm_2]
- fig.add_trace(go.Bar(x=FWHM_labels,y=FWHM_values, width = [0.35,0.35], showlegend=False,marker_color='black', name=''), row=2, col=2 )
-
- # Update xaxis properties
- fig.update_xaxes(row=2, col=2, showgrid=False, range=[0,3], linecolor='black', linewidth=2,
- tickmode='array', tickvals=[1,2], ticktext=['control', '{}-fold'.format(self._fold)], tickfont=dict(size=18))
-
- fig.update_yaxes(row=2, col=2, showgrid=False, range=[self.pathway_parameters['time_in'],round((max(FWHM_values)+(max(FWHM_values)-self.pathway_parameters['time_in'])*0.5)/5)*5],
- title_text='Time (s)', titlefont=dict(size=18), linecolor='black', linewidth=2,
- ticks='inside', ticklen=10, tickwidth=2, tickfont=dict(size=18))
-
- # Add diff lines
- FWHM_diffs = [max(FWHM_values) - v for v in FWHM_values]
- FWHM_diff_labels = dict(zip(FWHM_labels, FWHM_diffs))
- line_height = max(FWHM_values)+((max(FWHM_values)-self.pathway_parameters['time_in'])*0.30)
- fig.add_trace(go.Scatter(x=[1,1.5,2], y=[line_height]*3, mode = 'lines+text',showlegend=False, line=dict(color='black', width=1),name='',
- text=['', 'diff. = {} s'.format(round(FWHM_diffs[0], 3)),''], textposition='top center'), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[0]-0.175, FWHM_labels[0]+0.175], y=[FWHM_values[0]+(FWHM_values[0]*0.005)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[1]-0.175, FWHM_labels[1]+0.175], y=[FWHM_values[1]+(FWHM_values[1]*0.005)]*2, mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[0], FWHM_labels[0]], y=[FWHM_values[0]+(FWHM_values[0]*0.005), line_height], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
- fig.add_trace(go.Scatter(name='',x=[FWHM_labels[1], FWHM_labels[1]], y=[FWHM_values[1]+(FWHM_values[1]*0.005), line_height], mode = 'lines',showlegend=False, line=dict(color='black', width=1)), row=2, col=2)
-
-
- ####################
- #### FIGURE ####
- ####################
-
- fig.update_layout(height=1200, width=1300, title_text="", plot_bgcolor='white',showlegend=True,
- legend=dict(yanchor="top", x=0.3, y=.99,font=dict(family="sans-serif", size=14,color="black")))
- fig.update_annotations(font_size=20, font_color='black')
-
- if save==True:
- if filename==None:
- filename='plot.html'
- return pyoff.plot(fig, filename=filename)
- else:
- ext = os.path.splitext(filename)[-1]
- if ext == '.png': fig.write_image(filename, scale=3)
- elif ext == '.html': pyoff.plot(fig, filename=filename)
- else: raise TypeError("extension not valid. Use png or html.")
- elif save ==False: return fig
-
- return
-
--
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