Towards an Electronic Implementation of Membrane Computing: A Formal Description of Non-deterministic Evolution in Transition P Systems

Baranda, Angel V.; Arroyo, Fernando; Castellanos, Juan; Gonzalo, Rafael

doi:10.1007/3-540-48017-x_33

Cited by 5 publications

(3 citation statements)

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“…and [18], in Prolog. They were followed closely by [19] (in Scheme, based on the formalization presented in [20]) and [21] (in Haskell, based on the previous works analysing algorithmic aspects, the framework, and the data structures used, see [22][23][24][25][26]). After these ones, a new simulator for transition P systems using Java was presented in [27].…”

Section: First Steps Of Simulation In Membrane Computingmentioning

confidence: 99%

An interactive timeline of simulators in membrane computing

et al. 2019

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As with any fast-emerging research front in computer science, the proliferation of theoretical and practical results within Membrane computing since its appearance in 1998 was astonishing. As a consequence, it became necessary during the subsequent years to produce several surveys collecting the main achievements from a theoretical point of view, along with some specific surveys about simulation tools for this paradigm. As the discipline has reached a certain degree of maturity, more practical applications have arisen, and new collective works are summarising the new software products appeared. However, while these recapitulation efforts remain useful for details about new simulators, they cannot act as exhaustive updated listings, as they become obsolete as soon as new tools are developed. Thus, we considered that it was necessary to provide an interactive tool showing an updated timeline (https ://www.gcn.us.es/Simul ation MC) about the simulation of the computational devices of membrane computing (a.k.a P systems), aiming to stay updated whenever any new practical work comes out in the discipline. This paper recalls the main stages and milestones within the evolution of simulation tools for different types and variants of P systems, along with their main related applications. In addition, it describes the interactive web tool with the timeline mentioned, where all the references related here have been incorporated. Unlike other survey papers, it is the intent of this work to reinforce this initial collective effort with the web endpoint kept alive and updated.

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Section: First Steps Of Simulation In Membrane Computingmentioning

confidence: 99%

An interactive timeline of simulators in membrane computing

et al. 2019

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“…Several programming paradigms and programming languages were selected for implementing membrane systems simulators: Lisp, Haskell, MzScheme as functional programming languages (Suzuki and Tanaka, 2000;Arroyo et al, 2003;Noval et al, 2003;Baranda et al, 2002) prolog, CLIPS as declarative languages (Cordon-Franco et al, 2004;Perez-Jimenez and Romero-Campero, 2004), C, Visual C++, Java as imperative and object-oriented languages (Ciobanu and Paraschiv, 2002). Membrane computing was also described as executable specifications in Maude (Andrei et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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

Muniyandi

Zin

2010

Journal of Computer Science

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Problem statement: The evolution rules of membrane computing have been applied in a nondeterministic and maximally parallel way. In order to capture these characteristics, Gillespies 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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“…Almost all implementations consider deterministic P systems. There are several attempts to simulate P systems on the existing sequential computers: a Visual C++ simulation for P systems with active membranes and catalytic P systems allowing graphical simulation and step-by-step observations of the membrane system behavior is reported in [14]; a Java implementation is reported in [30]; an implementation of transition P systems in Haskell is discussed in [8,10]; another implementation of transition P system was done in MzScheme [9]; rewriting P systems and P systems with symport/antiport rules were described as executable specifications in Maude in [7,38]; in [36] the membrane system is programmed in VHDL; an implementation of Cayley P Systems was written in MGS [25]; transition P systems and deterministic P systems with active membranes were simulated also in Prolog [18,19,37]; recognizer P systems with active membranes, input membrane and external output were simulated in Clips and used to solve two NPcomplete problems in [33,34,35].…”

Section: Figure 3 a Generic P-system With Active Membranesmentioning

confidence: 99%

Parallel Jess

Petcu

The 4th International Symposium on Parallel and Distributed Computing (ISPDC'05)

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Parallel production or rule-based systems, like a parallel version of Jess, are needed for real applications. The proposed architecture for a such system is based on a wrapper allowing the cooperation between several instances of Jess running on different computers. The system has been designed having in mind the final goal to speedup current P system simulators. Preliminary tests show its efficiency in this particular case and on classical benchmarks.

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Towards an Electronic Implementation of Membrane Computing: A Formal Description of Non-deterministic Evolution in Transition P Systems

Cited by 5 publications

References 1 publication

An interactive timeline of simulators in membrane computing

An interactive timeline of simulators in membrane computing

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

Parallel Jess

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