Experimenting the Simulation Strategy of Membrane Computing with Gillespie Algorithm by Using Two Biological Case Studies

Abstract

Problem statement: The evolution rules of membrane computing have been applied in a nondeterministic and maximally parallel way. In order to capture these characteristics, Gillespie’s algorithm has been used as simulation strategy of membrane computing in simulating biological systems. Approach: This study was carried to discuss the simulation strategy of membrane computing with Gillespie algorithm in comparison to the simulation approach of ordinary differential equation by analyzing two biological case studies: prey-predator population and signal processing in the Ligand-Receptor Networks of protein TGF-β. Results: Gillespie simulation strategy able to confine the membrane computing formalism that used to represent the dynamics of prey-predator population by taking into consideration the discrete character of the quantity of species in the system. With Gillespie simulation of membrane computing model of TGF-β, the movement of objects from one compartment to another and the changes of concentration of objects in the specific compartments at each time step can be measured. Conclusion: The simulation strategy of membrane computing with Gillespie algorithm able to preserve the stochastic behavior of biological systems that absent in the deterministic approach of ordinary differential equation. However the performance of the Gillespie simulator should be improved to capture complex biological characteristics as well as to enhance the simulation processes represented by membrane computing model.