STOCHSIM: A Stochastic Simulator for (Bio)Chemical Reactions
STOCHSIM is a powerful stochastic simulator designed for (bio)chemical reactions. It enables the simulation of particle interactions represented as individual software objects, responding to probabilities derived from concentrations and rate constants. In the latest version 1.4 of STOCHSIM, we have introduced the capability to simulate simple two-dimensional spatial structures, allowing for nearest-neighbor interactions of molecules.
STOCHSIM was originally developed by Carl Morton-Firth and Tom Shimizu. Additionally, I have created TkSTOCHSIM, a versatile cross-platform graphical interface built using Perl/Tk.
For in-depth information about STOCHSIM, please visit the Cambridge Computational Cell Biology Group website.
You can download the program directly from the StochSim SourceForge site.
The program can be downloaded from the StochSim sourceforge site.
STOCHSIM and Systems Biology Markup Language (SBML)
“The Systems Biology Markup Language (SBML) is a standardized format for representing models of biochemical reaction networks. SBML can be applied to various domains within systems biology, including metabolic networks, cell-signaling pathways, genomic regulatory networks, and more.”
STOCHSIM offers support for SBML, though it’s essential to note that this support may not cover all aspects comprehensively. Here’s a breakdown:
Supported SBML Features:
- SBML1 and SBML2
- Simple complexes
Partially Supported SBML Features:
- Species initial amount and concentration
- Compartment volume and size
- Compartment spatial dimensions (limited to 3D)
- Species spatial size units
- Species boundary condition
Not Supported SBML Features:
- Multistate complexes
- Neighbor-sensitive complexes
- Rapid equilibriums
- Dynamic values
- Bi-dimensional lattices
- Various elements within SBML1 and SBML2
Examples of Models Built in STOCHSIM and Exported in SBML:
- Lotka-Volterra Model: This model represents a classical oscillating predator-prey system, akin to tyrosine-kinase receptor cascades.
A + X -> 2X ; k1
X + Y -> 2Y ; k2
Y -> Z ; k3
d[X]/dt = k1[A][X] – k2[X][Y]
d[Y]/dt = k2[X][Y] – k3[Y]
Visualisation of Y1 and Y2 should give:
Lotka Volterra simulation oscillating curve.
2. Michaelis-Menten Model: Describing a simple enzymatic reaction (phosphorylation).
E + S <=> ES ; k1,k2
ES -> E + P ; k3
d[S]/dt = k2[ES] – k1[E][S]
d[P]/dt = k3[ES]
d[ES]/dt = k1[E][S] – (k2+k3)[ES]
Visualisation of all complexes should give:
Michalis Menten enzymatic reaction.
3. Excitatory Postsynaptic Potential (EPSP) Model: Generated by the application of acetylcholine on a group of nicotinic acetylcholine receptors (Torpedo electroplax. Cf. Edelstein et al. (1996). Biol Cybern, 75: 361-379).
B <=> A <=> I <=> D
/| /| /| /|
|/ |/ |/ |/
BL <=> AL <=> IL <=> DL
/| /| /| /|
|/ |/ |/ |/
BLL <=> ALL <=> ILL <=> DLL
Visualisation of monoliganded (BL, AL, DL, IL), biliganded (BLL, ALL, ILL, DLL), open (A, AL, ALL) and desensitized (I, IL, ILL, D, DL, DLL) complexes should give:
In the previous model, the acetylcholine was modeled as a reactant of constant concentration. In the present one, acetycholine is applied as a square signal (actualy it is modeled as a dynamic modification of the binding kon parameters). SBML2
nAChR EPSP after a square pulse of ACh.