Membrane System-Based Models for Specifying Dynamical Population Systems

Colomer-Cugat, M. A.; García-Quismondo, Manuel; Macías-Ramos, Luis F.; Amor, Miguel Ángel Martínez del; Pérez–Hurtado, Ignacio; Pérez–Jiménez, Mario J.; Riscos–Núñez, Agustín; Valencia-Cabrera, Luis

doi:10.1007/978-3-319-03191-0_4

Cited by 8 publications

(12 citation statements)

References 27 publications

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“…In this sense, the consistency of charges must be maintained: in order to simultaneously apply several rules from R Π j to the same membrane, all rules must have the same electrical charge on their right-hand side. For more information about the syntax of the models, refer to [9,31]. The semantics discussed in this section are the basics of the functioning of the framework.…”

Section: Definition 1 a Population Dynamics P System Of Degree (Q M)mentioning

confidence: 99%

“…Thus, researchers can write and debug P systems in a familiar way using modules, parameters, variables, iterators and other programming ingredients. For instance, several real ecosystem models have been written, debugged and simulated using the P-Lingua framework [9,15,31].…”

Section: P-lingua and Plinguacore Version 4 P-linguamentioning

confidence: 99%

“…Then, a brief pseudocode and explanation of the algorithm is provided. For more information, please refer to [31,32]. Firstly, rules in R and R E can be classified into consistent blocks having the same left-hand side and the same charge in the right-hand side, following the Definitions 2 and 3.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…[20]). Generic simulators are required in certain scenarios, such as in ecosystem modelling, so that the model designers would use just one simulator for all their models [31]. However, this comes at the expense of performance degradation, because the simulators are developed to support many worst case-scenarios that normally do not take place in models.…”

Section: Introductionmentioning

confidence: 99%

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Adaptative parallel simulators for bioinspired computing models

Amor

Pérez–Hurtado

Orellana-Martín

et al. 2020

Future Generation Computer Systems

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In the Membrane Computing area, P systems are unconventional devices of computation inspired by the structure and processes taking place in living cells. Main successful P system applications lie in computability and computational complexity theories, as well as in biological modelling. Given that models become too complex to deal with, simulators for P systems are essential tools and their efficiency is critical. In order to handle the diverse situations that may arise during the computation, these simulators have to take into account that worst-case scenarios can happen, even though they rarely occur. As a result, there is a significant loss of performance. In this paper, the concept of adaptative simulation for P systems is introduced to palliate this problem. This is achieved by passing high-level information provided directly by P system model designers to the simulator, helping it to better adapt to the target model. For this purpose, an existing simulator for an ecosystem modelling framework, named Population Dynamics P systems, is extended to include the information of modules, that are usually employed to define ecosystem models. Moreover, the standard description language for P systems, P-Lingua, has been re-engineered in its version 5. It now includes a new syntactical item, called feature, to express this kind of high-level semantic information. Experiments show that this simple adaptative simulator supporting modules as features doubles the performance when running on GPUs and on multicore processors.

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Section: Definition 1 a Population Dynamics P System Of Degree (Q M)mentioning

confidence: 99%

Section: P-lingua and Plinguacore Version 4 P-linguamentioning

confidence: 99%

Section: Algorithm Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptative parallel simulators for bioinspired computing models

Amor

Pérez–Hurtado

Orellana-Martín

et al. 2020

Future Generation Computer Systems

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“…Although each of these (extended) computational frameworks has been used as a modeling framework for many scenarios, none of them fully integrates the dynamics and structural details of complex systems (Colomer-Cugat et al, 2014). In this concern, P systems provide the following advantages as a computational modeling framework: (a) flexibility, i.e.…”

Section: Computational Modeling Framework In Population Dynamicsmentioning

confidence: 99%

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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