Probabilistic Guarded P Systems, A New Formal Modelling Framework

García-Quismondo, Manuel; Amor, Miguel Ángel Martínez del; Pérez–Jiménez, Mario J.

doi:10.1007/978-3-319-14370-5_12

Cited by 8 publications

(10 citation statements)

References 23 publications

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“…A later contribution, by 2014, to the practical application of membrane computing in biology, making use of P-Lingua framework, was MeCoGUI [159], incorporating probabilistic guarded P systems [107]. This application was used recently for the simulation of the ecosystem of Pieris napi oleracea in eastern North America [108,161].…”

Section: The Era Of Practical Applications Based On P Systemsmentioning

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: The Era Of Practical Applications Based On P Systemsmentioning

confidence: 99%

An interactive timeline of simulators in membrane computing

et al. 2019

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“…This framework has been applied to model the dynamics of several animal species, such as of the zebra mussel Dreissena polymorpha , the avian scavengers in the Pyrenees (Colomer et al, 2011b) and the Pyrenean Chamois (Rupicapra p. pyrenaica) (Colomer et al, 2011a). PGP systems (and so, PGSP) are multienvironment P systems (García-Quismondo et al, 2015) where each environment is elemental (i.e. do not contain a inner additional structure), and the contexts are represented by flags, and discriminate between evolution and context-changing rules.…”

Section: Computational Modeling Framework In Population Dynamicsmentioning

confidence: 99%

“…Probabilistic Guarded P systems (PGP systems, for short) (García-Quismondo et al, 2015) provide a membrane computingbased modeling framework for population dynamics. Roughly speaking, a PGP system consists of a network of environments such that each environment is identified by a unique label and contains a multiset of objects of a common working alphabet.…”

Section: Probabilistic Guarded Scripted P (Pgsp) Systemsmentioning

confidence: 99%

“…P-Lingua is a Java (Oracle Corporation, 2017) API that provides a plugin-based architecture to integrate new membrane computing frameworks in a straightforward manner, as well as a description language for defining P systems on plain text files. We specified the model according the P-Lingua syntax for PGP systems (García-Quismondo et al, 2015). We have developed a simulator for PGSP systems and integrated them into P-Lingua.…”

Section: Kfs Klsmentioning

confidence: 99%

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Evolutionary response of a native butterfly to concurrent plant invasions: Simulation of population dynamics

García-Quismondo

Reed

Chew

et al. 2017

Ecological Modelling

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The habitat of the green-veined white butterfly Pieris oleracea in eastern North America has undergone invasions by the exotic plant garlic mustard (Alliaria petiolata), which is replacing native hosts of P. oleracea such as Cardamine diphylla. A. petiolata was originally lethal to most larvae of the native butterfly but during the past 20+ years it has been incorporated successfully into the larval diet, likely through evolutionary change. The region was also invaded by another exotic plant, Cardamine pratensis, on which the native butterfly larvae readily develops, allowing the possibility of population rescue. Further complicating the butterfly's reproductive dynamics, it is multigenerational within a summer, and host plant availability and location change during the summer. Our goal is to model the expected dynamics of the native butterfly population in this evolving, dynamic landscape by using a new bio-inspired paradigm known as membrane computing.In this context, a Probabilistic Guarded Scripted P system has been designed to model and explore the conditions under which an allele conferring ability of P. oleracea larvae to develop on A. petiolata might have proliferated. The design describes a population dynamics model whose parameter values are derived from experimental and observational data. Our modeling framework is spatially explicit and our model integrates seasonal as well as annual dynamics. The simulation results from our model qualitatively match our field observations and experimental laboratory results, and agree with the results from a previous model on the genotypic adaptation of this butterfly species. From the simulations we identified the likely trajectories for the spatio-temporal distribution of alleles enabling P. oleracea to use the invasive plant species across this selective and phenological mosaic.

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“…Further works of modelling biological systems using membrane computing can be seen in Cardona et al [21,22], which look at the dynamics of ecosystems. To address the inherent randomness and uncertainty in large biological systems, multi-environment P systems have been combined with probabilistic terms [23,24].…”

Section: Introductionmentioning

confidence: 99%

A P system model of swarming and aggregation in a Myxobacterial colony

Nash

Kalvala

2019

J Membr Comput

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Bacterial communities provide an interesting subject for the study of emergence and complexity as the consequence of many local interactions. In particular, the soil-dwelling social bacterium Myxobacteria demonstrates two distinct types of motility, social motility via the sensing of bacterial slime deposits and adventurous motility. Both modes of motility are governed by local interactions. Using P systems, a membrane computing methodology based on compartmental rewrite rules for modelling computational processes; this work demonstrates how minimal set of rules can model swarming and aggregating behaviour in Myxobacteria bacterial populations. Our model uses a multi-environment P system similar a 2D cellular automaton to represent the substrate environment whilst stochastic rule selection dictates Myxobacterial motion according to behaviour observed in vitro. The rules account for both mechanisms of motility, the deposit and detection of slime, a change in direction due to C-signal induction and the mixing of population numbers. Simulations demonstrate an extensible computational framework for the modelling of bacterial behaviour, with the potential for extension into additional emergent behaviours.

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Probabilistic Guarded P Systems, A New Formal Modelling Framework

Cited by 8 publications

References 23 publications

An interactive timeline of simulators in membrane computing

An interactive timeline of simulators in membrane computing

Evolutionary response of a native butterfly to concurrent plant invasions: Simulation of population dynamics

A P system model of swarming and aggregation in a Myxobacterial colony

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