diff --git a/.ipynb_checkpoints/SSBtoolkit-Tutorial1-checkpoint.ipynb b/.ipynb_checkpoints/SSBtoolkit-Tutorial1-checkpoint.ipynb
index 891f0ec81ac74369ac96215a8f2e7effe2b6c57d..d93b89b7239f0ffaee688591ec9ddd12dc9f66ed 100644
--- a/.ipynb_checkpoints/SSBtoolkit-Tutorial1-checkpoint.ipynb
+++ b/.ipynb_checkpoints/SSBtoolkit-Tutorial1-checkpoint.ipynb
@@ -12,7 +12,7 @@
"\n",
"<br><br><br><br>\n",
"\n",
- "# Simulating dose-response curves from agonist affinity values\n",
+ "# Simulation of dose-response curves from affinity values\n",
"\n",
"In this tutorial we will simulate a mathematical model of a signaling pathway to obtain dose-response curves, from wich *potency (EC<sub>50</sub>)* values of agonists can be infered. \n",
"\n",
diff --git a/.ipynb_checkpoints/SSBtoolkit-Tutorial2-checkpoint.ipynb b/.ipynb_checkpoints/SSBtoolkit-Tutorial2-checkpoint.ipynb
index 044c3cd008b3b00de429927d408c8348bf4a8bc4..f71f5a67187d9f5f3104366b45c745374fce9e90 100644
--- a/.ipynb_checkpoints/SSBtoolkit-Tutorial2-checkpoint.ipynb
+++ b/.ipynb_checkpoints/SSBtoolkit-Tutorial2-checkpoint.ipynb
@@ -11,8 +11,9 @@
"</div> \n",
"\n",
"<br><br><br><br>\n",
+ "<br>\n",
"\n",
- "# Predicting dose-response curves of antagonists\n",
+ "# Simulation of dose-response curves of antagonists\n",
"\n",
"In this tutorial we will simulate a mathematical model of a signaling pathway to obtain dose-response curves of antagonists, from wich their *potencies (IC$_{50}$)* can be infered. \n",
"\n",
diff --git a/.ipynb_checkpoints/SSBtoolkit-Tutorial3A-checkpoint.ipynb b/.ipynb_checkpoints/SSBtoolkit-Tutorial3A-checkpoint.ipynb
index ee851b3b69d4534b2be42d5c56d923c4e6e69eb1..a752599232446efd32a081d6e47991ffd2649071 100644
--- a/.ipynb_checkpoints/SSBtoolkit-Tutorial3A-checkpoint.ipynb
+++ b/.ipynb_checkpoints/SSBtoolkit-Tutorial3A-checkpoint.ipynb
@@ -11,8 +11,9 @@
"</div> \n",
"\n",
"<br><br><br><br>\n",
+ "<br>\n",
"\n",
- "# Simulating dose-response curves from ligand kinetic values\n",
+ "# Simulation of dose-response curves from kinetic values\n",
"\n",
"In this tutorial we will simulate mathematical model of a signaling pathway to obtain dose-response curves, and consequently, predict the *efficacy (EC$_{50}$)* of drugs. \n",
"\n",
diff --git a/.ipynb_checkpoints/SSBtoolkit-Tutorial3B-tauRAMD-checkpoint.ipynb b/.ipynb_checkpoints/SSBtoolkit-Tutorial3B-tauRAMD-checkpoint.ipynb
index 21dc63feb75c84266dfe7ca109ef8905da282ada..7e01490feb1e6daf42366058ed40e3c69db693df 100644
--- a/.ipynb_checkpoints/SSBtoolkit-Tutorial3B-tauRAMD-checkpoint.ipynb
+++ b/.ipynb_checkpoints/SSBtoolkit-Tutorial3B-tauRAMD-checkpoint.ipynb
@@ -12,7 +12,7 @@
"<br><br><br><br>\n",
"<br>\n",
"\n",
- "# Predicting dose-response cruves from data acquired with tauRAMD \n",
+ "# Simulation of dose-response cruves from data acquired with tauRAMD \n",
"\n",
"\n",
"In this notebook we will a simulate mathematical model of a signaling pathway to obtain dose-response curves using kinetic values obtained with the <b>tauRAMD</b> tool.\n",
diff --git a/README.md b/README.md
index 50ac3f26497dfa9c9abb8280bb69c817cd0a1c43..c826145c29c470443d7f7ee4d5b20121f4b45ac8 100644
--- a/README.md
+++ b/README.md
@@ -32,3 +32,26 @@ How to do:
5. [Exploring SSB pathways associated to disease variants](SSBtoolkit-Tutorial4_OXTR.ipynb)
+
+
+## Cite Us
+If you use or adapt the SSBtoolkit for your own research projects please cite us.
+
+```
+@article{XXX,
+ title={XXX},
+ author={XXX},
+ publisher={XXX},
+ note={\url{XXX}},
+ year={XXX}
+}
+```
+
+## Acknowledgments
+
+EU Human Brain Project (SGA1 and SGA2): This open source software was developed in part in the Human Brain Project funded from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No 720270 and No. 78907 (Human Brain Project SGA1 and SGA2).
+
+<div style="padding-bottom:50px">
+<img src="https://res.cloudinary.com/djz27k5hg/image/upload/v1637657234/logos/HBP_horizontal_logo_qtcyzn.png" width="300" align='left' style="margin-left:50px">
+ <img src="https://res.cloudinary.com/djz27k5hg/image/upload/v1642677502/logos/COFUNDED_EU_j2ktlp.jpg" width="300" align='left' style="margin-left:50px">
+</div>
\ No newline at end of file
diff --git a/SSBtoolkit-Tutorial1.ipynb b/SSBtoolkit-Tutorial1.ipynb
index 891f0ec81ac74369ac96215a8f2e7effe2b6c57d..d93b89b7239f0ffaee688591ec9ddd12dc9f66ed 100644
--- a/SSBtoolkit-Tutorial1.ipynb
+++ b/SSBtoolkit-Tutorial1.ipynb
@@ -12,7 +12,7 @@
"\n",
"<br><br><br><br>\n",
"\n",
- "# Simulating dose-response curves from agonist affinity values\n",
+ "# Simulation of dose-response curves from affinity values\n",
"\n",
"In this tutorial we will simulate a mathematical model of a signaling pathway to obtain dose-response curves, from wich *potency (EC<sub>50</sub>)* values of agonists can be infered. \n",
"\n",
diff --git a/SSBtoolkit-Tutorial2.ipynb b/SSBtoolkit-Tutorial2.ipynb
index 044c3cd008b3b00de429927d408c8348bf4a8bc4..f71f5a67187d9f5f3104366b45c745374fce9e90 100644
--- a/SSBtoolkit-Tutorial2.ipynb
+++ b/SSBtoolkit-Tutorial2.ipynb
@@ -11,8 +11,9 @@
"</div> \n",
"\n",
"<br><br><br><br>\n",
+ "<br>\n",
"\n",
- "# Predicting dose-response curves of antagonists\n",
+ "# Simulation of dose-response curves of antagonists\n",
"\n",
"In this tutorial we will simulate a mathematical model of a signaling pathway to obtain dose-response curves of antagonists, from wich their *potencies (IC$_{50}$)* can be infered. \n",
"\n",
diff --git a/SSBtoolkit-Tutorial3A.ipynb b/SSBtoolkit-Tutorial3A.ipynb
index ee851b3b69d4534b2be42d5c56d923c4e6e69eb1..a752599232446efd32a081d6e47991ffd2649071 100644
--- a/SSBtoolkit-Tutorial3A.ipynb
+++ b/SSBtoolkit-Tutorial3A.ipynb
@@ -11,8 +11,9 @@
"</div> \n",
"\n",
"<br><br><br><br>\n",
+ "<br>\n",
"\n",
- "# Simulating dose-response curves from ligand kinetic values\n",
+ "# Simulation of dose-response curves from kinetic values\n",
"\n",
"In this tutorial we will simulate mathematical model of a signaling pathway to obtain dose-response curves, and consequently, predict the *efficacy (EC$_{50}$)* of drugs. \n",
"\n",
diff --git a/SSBtoolkit-Tutorial3B-tauRAMD.ipynb b/SSBtoolkit-Tutorial3B-tauRAMD.ipynb
index 21dc63feb75c84266dfe7ca109ef8905da282ada..7e01490feb1e6daf42366058ed40e3c69db693df 100644
--- a/SSBtoolkit-Tutorial3B-tauRAMD.ipynb
+++ b/SSBtoolkit-Tutorial3B-tauRAMD.ipynb
@@ -12,7 +12,7 @@
"<br><br><br><br>\n",
"<br>\n",
"\n",
- "# Predicting dose-response cruves from data acquired with tauRAMD \n",
+ "# Simulation of dose-response cruves from data acquired with tauRAMD \n",
"\n",
"\n",
"In this notebook we will a simulate mathematical model of a signaling pathway to obtain dose-response curves using kinetic values obtained with the <b>tauRAMD</b> tool.\n",
diff --git a/docs/ssb_toolkit.md b/docs/ssb_toolkit.md
index b916f509e3534bfdd6d81ff9a6baa0c278f37de6..87aa9d373c6ed102f10d71c218541e424a8d4803 100644
--- a/docs/ssb_toolkit.md
+++ b/docs/ssb_toolkit.md
@@ -126,7 +126,7 @@ All the three signaling pathways were developed using the [PySB](https://pysb.or
To predict a dose-response curve from the simulation of signaling pathways, individual simulations of the pathway according to an array of ligand concentrations must be performed first. The dose-response curve is, then, obtained by fitting a logistic regression (eq.4) to the maximum response values from each individual simulation. In the end, a curve of the response in function of the ligand concentration is obtained (Fig.7). The response of a signaling pathway is, naturally, represented by the increase or decrease of one of the species described by the model. Therefore, for each signaling pathway we defined, by default, a reference species. While cAMP was chosen as reference species for the G<sub>s</sub> and G<sub>i/o</sub> pathway, for the G<sub>q/11</sub> pathway we chose IP<sub>3</sub> , Fig.5.
<br /> <br />
<figure>
-<img src="./img/fig7.png" alt='equation' width="800" >
+<img src="./img/fig7.png" alt='scheme' width="800" >
<figcaptation align='center'>Fig. 7 - Conceptual scheme for predicting dose-response through signaling pathways’ simulation. For each concentration value the signaling model is simulated obtaining in the end several curve of the concentration of a specific species of the pathway as function of time. After, the maximum value of each curve is selected and plotted, resulting, in the end, in the dose-response curve.</figcaptation >
</figure>
<br /> <br />