Problem statement: Membrane computing formalism has provided better modeling capabilities for biological systems in comparison to conventional mathematical models. Model checking could be used to reason about the biological system in detail and with precision by verifying formally whether membrane computing model meets the properties of the system. Approach: This study was carried to investigate the preservation of properties of two biological systems that had been modeled and simulated in membrane computing by a method of model checking using PRISM. The two biological systems were prey-predator population and signal processing in the legend-receptor networks of protein TGF-ß. Results: The model checking of membrane computing model of the biological systems with five different properties showed that the properties of the biological systems could be preserved in the membrane computing model. Conclusion: Membrane computing model not only provides a better approach in representing and simulating a biological system but also able to sustain the basic properties of the system
Problem statement: Discrete systems have been modeled by using Ordinary Differential Equation (ODE) in which the variation of concentration of an object was modeled as continuous and deterministic manner, contrary to the real behaviors of such systems. Although, this approaches able to generate the general behavior of the system, the specific discrete processes and stochastic behaviors in the system have not been addressed. Membrane computing has been an unconventional computational approach that provides a platform for modeling discrete systems. It deals with parallel, distributed and non-deterministic computing models. Approach: This study was carried to compare the ODE with membrane computing approach in modeling a discrete system by taking Prey-Predator population as the case study. Membrane computing simulator based on Gillespie Algorithm and Probabilistic and Symbolic Model Checker (PRISM) were used to verify and validate the model. Results: Membrane computing able to not only maintain the dynamics and equilibrium of Prey-Predator population but also preserve the discrete and stochastic evolvement of the prey and predator in the population by sustaining the properties of the system. Conclusion: Membrane computing modeling approach preserved the characteristics of discrete systems that absent in the ODE approach
Problem statement: Most of the biological systems have been hierarchical in structure with processes interacting between different compartments. Membrane computing formalism has provided modeling capabilities in representing the structure of biological systems. Approach: This study was carried to investigate the modeling of a multi-compartment biological system by using membrane computing formalism. The hormone-induced calcium oscillations in liver cells which was modeled with ordinary differential equation was used as a case study. The membrane computing model of this case study was verified and validated by using simulation strategy of Gillespie algorithm and the method of model checking using probabilistic symbolic model checker. The results of membrane computing model were compared to the ordinary differential equation model. Results: The simulation and model checking of membrane computing model of the biological case study showed that the properties of the multi-compartments biological system could be preserved with the membrane computing model. Membrane computing model could also accommodate the structure and processes of the multi-compartments biological system which were absent in the ordinary differential equation model. Conclusion: Membrane computing model provides a better approach in representing a multi-compartment system and able to sustain the basic properties of the system. However appropriate value of parameters to represent the rules of the processes of the membrane computing model to manage the stochastic behavior should be formulated to meet the performance of the biological system
Membrane computing is a field in computer science that is inspired from the structure and the processes of living cells and is being considered as an alternative in solving the limitations in conventional mathematical approaches by taking into consideration its essential features that are of interest for research in systems biology. Advancements in computability make it feasible to handle huge volumes of data in biology and propose a new and better approach using a discreet computer science model, such as membrane computing. In this respect, membrane-computing abilities, to enhance the understanding of the system level of biological systems, have been explored. This study discusses experiences in applying membrane computing in modeling biological systems as well as possibilities of incorporating membrane computing into other computer science paradigms to enhance the use of membrane computing in systems biology. Experiences in modeling aspects of systems biology with membrane computing demonstrate additional advantages and possibilities compared with conventional methods. However, they are not yet used widely to model or simulate biological processes or systems. A general framework of modeling and verifying biological systems using membrane computing is essential as a guideline for biologists in their research in systems biology
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