Experimenting the simulation strategy of membrane computing with Gillespie algorithm by using two biological case studies

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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.

Original languageEnglish
Pages (from-to)525-535
Number of pages11
JournalJournal of Computer Science
Volume6
Issue number5
DOIs
Publication statusPublished - 2010

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Membranes
Biological systems
Ordinary differential equations
Signal processing
Simulators
Ligands
Proteins

Keywords

  • Biological system
  • Gillespie algorithm
  • Membrane computing

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Artificial Intelligence

Cite this

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title = "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.",
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AU - Mohd. Zin, Abdullah

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AB - 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.

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