Computational Modeling for the Activation Cycle of G-proteins by G-protein-coupled Receptors

Student: Yifei Bao1, Tommy E. White2,Advisor: Adriana B. Compagnoni1, Joseph S. Glavy2

Department of Computer Science1, Department of Chemistry, Chemical Biology and Biomedical Engineering2, Schaefer School of Engineering and Science, Stevens Institute of Technology, Hoboken, NJ 07030

IntroductionWe present three new computational models of the activation cycle of G-proteins that regulate cellular signaling events downstream of G-protein-coupled receptors (GPCRs), and we demonstrate the advantage in the simplicity of modeling G-protein signaling over methods using traditional Ordinary Differential Equations (ODEs). We implement the G-protein cycle in the stochastic Pi-calculus using SPiM, as Petri-nets using Cell Illustrator, and in theKappa Language using Cellucidate.

We also provide a high-level notationto abstract away from communicationprimitives that may be unfamiliar to the average biologist, and we showhow to translate it into stochastic Pi-calculus processes (not shown).

Results

Methods1. ODEs Modeling

2. Stochastic Pi-Calculus ModelingThe stochastic Pi-calculus is aprocess algebra wherestochastic rates are imposed on processes, allowing for more accurate description of biological systems. A process can be depicted as a collection of interacting automata with two kinds of reactions: delay@r and interaction@r on ch.

4. Kappa Languange ModelingIn the Kappa language, reaction rules are described by rewriting rules between lists of agents. Each agent has a name and binding sites. Agents can become bound, and the two end points of a link between two agents is Indicated by !i, for some index ~value specifies the internal state of a site on the agent, -> specifies a bidirectional reaction, <-> specifies an unidirectional reaction, and @value specifies the reactionrate.

ConclusionIn our study, the models we build using stochastic modelingapproaches can represent the G-protein cycle quite convincingly, which shows that stochastic modeling approaches could be efficient instruments to assist in biomedical research.

We develop a high level notation that can be systematically translated into SPiM programs to hide Pi-calculus communication primitives and enable modeling using only terminology directly obtained from biological processes (not shown).

Research & Entrepreneurship Day2010

Fig.1 Activation cycle of G-proteins by G-protein-coupled receptors.

Fig.4 ODEs simulation by Matlab. Fig.5 Pi-calcucus simulation by SPiM.

Fig.7 PetriNets simulation by Cell Illustrator .

Fig.6 Kappa simulation by Cellucidate .

Methods4. Petri Nets ModelingThe basic Petri Net is a directedbipartite graph with two kindsof nodes which are either placesor transitions and directed arcs which connect nodes. Inmodeling biological processes, place nodes represent molecular species and transition nodes represent reactions.

Fig.3 Petri Nets for G-protein Cycle.

Fig.2 Graphical representation of Stochastic Pi-calculus modeling .