Kinetikit 12: Interface for chemical kinetic models in MOOSEGUI

Upi Bhalla

Harsha Rani

Dec 27, 2014

Introduction

Kinetikit 12 is a graphical interface for doing chemical kinetic modeling in MOOSE. It is derived in part from Kinetikit, which was the graphical interface used in GENESIS for similar models. Kinetikit, also known as kkit, was at version 11 with GENESIS. Here we start with Kinetikit 12.

TODO What are chemical kinetic models?

Much of neuronal computation occurs through chemical signaling. For example, many forms of synaptic plasticity begin with calcium influx into the synapse, followed by calcium binding to calmodulin, and then calmodulin activation of numerous enzymes. These events can be represented in chemical terms:

4 Ca2+ + CaM <===> Ca4.CaM

Such chemical equations can be modeled through standard Ordinary Differential Equations, if we ignore space:

d[Ca]/dt = −4Kf ∗ [Ca]4 ∗ [CaM] + 4Kb ∗ [Ca4.CaM] d[CaM]/dt = −Kf ∗ [Ca]4 ∗ [CaM] + Kb ∗ [Ca4.CaM] d[Ca4.CaM]/dt = Kf ∗ [Ca]4 ∗ [CaM] − Kb ∗ [Ca4.CaM]

MOOSE models these chemical systems. This help document describes how to do such modelling using the graphical interface, Kinetikit 12.

Levels of model

Chemical kinetic models can be simple well-stirred (or point) models, or they could have multiple interacting compartments, or they could include space explicitly using reaction-diffusion. In addition such models could be solved either deterministically, or using a stochastic formulation. At present Kinetikit handles compartmental models but does not compute diffusion within the compartments, though MOOSE itself can do this at the script level. Kkit12 will do deterministic as well as stochastic chemical calculations.

Numerical methods

Using Kinetikit 12

Overview

<!--* Save plots using the icons at the bottom of the Plot Window.

Most of these operations are detailed in other sections, and are shared with other aspects of the MOOSE simulation interface. Here we focus on the Kinetikit-specific items.

Model layout and icons

When you are in the Model View tab you will see a collection of icons, arrows, and grey boxes surrounding these. This is a schematic of the reaction scheme being modeled. You can view and change parameters, and change the layout of the model.

Resizing the model layout and icons:

Compartment

The compartment in moose is usually a contiguous domain in which a certain set of chemical reactions and molecular species occur. The definition is very closely related to that of a cell-biological compartment. Examples include the extracellular space, the cell membrane, the cytosol, and the nucleus. Compartments can be nested, but of course you cannot put a bigger compartment into a smaller one.

Pool

This is the set of molecules of a given species within a compartment. Different chemical states of the same molecule are in different pools.

Buffered pools

Some pools are set to a fixed n, that is number of molecules, and therefore a fixed concentration, throughout a simulation. These are buffered pools.

Reaction

These are conversion reactions between sets of pools. They are reversible, but you can set either of the rates to zero to get irreversibility. In the illustration below, D and A are substrates, and B is the product of the reaction. This is indicated by the direction of the green arrow.

Mass-action enzymes

These are enzymes that model the chemical equations

E + S <===> E.S —> E + P

Note that the second reaction is irreversible. Note also that mass-action enzymes include a pool to represent the E.S (enzyme-substrate) complex. In the example below, the enzyme pool is named MassActionEnz, the substrate is C, and the product is E. The direction of the enzyme reaction is indicated by the red arrows.

Michaelis-Menten Enzymes

These are enzymes that obey the Michaelis-Menten equation

V = Vmax * [S] / ( Km + [S] ) = kcat * [Etot] * [S] / ( Km + [S] )

where

Nominally these enzymes model the same chemical equation as the mass-action enzyme:

E + S <===> E.S —> E + P

but they make the assumption that the E.S is in a quasi-steady-state with E and S, and they also ignore sequestration of the enzyme into the complex. So there is no representation of the E.S complex. In the example below, the enzyme pool is named MM_Enz, the substrate is E, and the product is F. The direction of the enzyme reaction is indicated by the red arrows.

Function

Function objects can be used to evaluate expressions with arbitrary number of variables and constants. We can assign expression of the form:

f(c0, c1, ..., cM, x0, x1, ..., xN, y0,..., yP )

where ci‘s are constants and xi‘s and yi‘s are variables.

It can parse mathematical expression defining a function and evaluate it and/or its derivative for specified variable values. The variables can be input from other moose objects. In case of arbitrary variable names, the source message must have the variable name as the first argument.

Model operations

Model building