Manuel García-Quismondo

An overview of p-lingua 2.0

P–Lingua is a programming language for membrane computing which aims to be a standard to define P systems. In order to implement this idea, a Java library called pLinguaCore has been developed as a software framework for cell–like P systems. It is able to handle input files (either in XML or in P–Lingua format) defining P systems from a number of different cell–like P system models. Moreover, the library includes several built–in simulators for each supported model. For the sake of software portability, pLinguaCore can export a P system definition to any convenient output format (currently XML and binary formats are available). This software is not a closed product, but it can be extended to accept new input or output formats and also new models or simulators. The term P–Lingua 2.0 refers to the software package consisting of the above mentioned library together with a user interface called pLinguaPlugin (more details can be found at http://www.p-lingua.org). Finally, in order to illustrate the software, this paper includes an application using pLinguaCore for describing and simulating ecosystems by means of P systems.

3-Col problem modelling using simple kernel P systems

This paper presents the newly introduced class of (simple) kernel P systems ((s)kP systems) and investigates through a 3-colouring problem case study the expressive power and efficiency of kernel P systems. It describes two skP systems that model the problem and analyses them in terms of efficiency and complexity. The skP models prove to be more succinct (in terms of number of rules, objects, number of cells and execution steps) than the corresponding tissue P system, available in the literature, that solves the same problem, at the expense of a greater length of the rules.

A p---lingua based simulator for spiking neural p systems

The research within the field of Spiking Neural P systems (SN P systems, for short) is focusing mainly in the study of the computational completeness (they are equivalent in power to Turing machines) and computational efficiency of this kind of systems. These devices have been shown capable of providing polynomial time solutions to computationally hard problems by making use of an exponential workspace constructed in a natural way. In order to experimentally explore this computational power, it is necessary to develop software that provides simulation tools (simulators) for the existing variety of SN P systems. Such simulators allow us to carry out computations of solutions to NP-complete problems on certain instances. Within this trend, P-Lingua provides a standard language for the definition of P systems. As part of the same project, pLinguaCore library provides particular implementations of parsers and simulators for the models specified in P-Lingua. In this paper, an extension of the P-Lingua language to define SN P systems is presented, along with an upgrade of pLinguaCore including a parser and a new simulator for the variants of these systems included in the language.

DCBA: simulating population dynamics p systems with proportional object distribution

Population Dynamics P systems provide a formal framework for ecological modelling having a probabilistic (while keeping the maximal parallelism). Several simulation algorithms have been developed always trying to reach higher reliability in the way they reproduce the behaviour of the ecosystems being modelled. It is natural for those algorithms to classify the rules into blocks, comprising rules that share identical left-hand side. Previous algorithms, such as the Binomial Block Based (BBB) or the Direct Non Deterministic distribution with Probabilities (DNDP), do not define a deterministic behaviour for blocks of rules competing for the same resources. In this paper we introduce the Direct distribution based on Consistent Blocks Algorithm (DCBA), a simulation algorithm which addresses that inherent non-determinism of the model by distributing proportionally the resources.

Simulating P Systems on GPU Devices: A Survey

P systems have been proven to be useful as modeling tools in many fields, such as Systems Biology and Ecological Modeling. For such applications, the acceleration of P system simulation is often desired, given the computational needs derived from these kinds of models. One promising solution is to implement the inherent parallelism of P systems on platforms with parallel architectures. In this respect, GPU computing proved to be an alternative to more classic approaches in Parallel Computing. It provides a low cost, and a manycore platform with a high level of parallelism. The GPU has been already employed to speedup the simulation of P systems. In this paper, we look over the available parallel P systems simulators on the GPU, with special emphasis on those included in the PMCGPU project, and analyze some useful guidelines for future implementations and developments.

Modeling regenerative processes with membrane computing

A model in membrane computing is proposed to explain regeneration in planarians.The model recapitulates the outcomes of a set of planarian surgical experiments.Membrane computing represents a novel paradigm to model regeneration mechanisms. Understanding the remarkable ability of some organisms to restore their anatomical shape following the amputation of large parts of their bodies is currently a major unsolved question in regenerative biology and biomedicine. Despite rapid advances in the molecular processes required for regeneration, a systems level, algorithmic understanding of this process has remained elusive. For this reason, the field needs new computational paradigms to help model the flow of information during regeneration. Membrane computing is a branch of natural computing that studies the properties and applications of theoretical computing devices known as P systems. These systems are an abstraction of the structure and functioning of a living cell, as well as its organization in tissues. Here, we propose a model of regenerative processes in planarian worms based on P systems, which recapitulates several aspects of regenerative pattern regulation. Our results demonstrate that it is possible to apply a novel computational framework to help understand pattern regulation in regenerative biology.

Probabilistic Guarded P Systems, A New Formal Modelling Framework

Multienvironment P systems constitute a general, formal framework for modelling the dynamics of population biology, which consists of two main approaches: stochastic and probabilistic. The framework has been successfully used to model biologic systems at both micro (e.g. bacteria colony) and macro (e.g. real ecosystems) levels, respectively.

Membrane System-Based Models for Specifying Dynamical Population Systems

Population Dynamics P systems (PDP systems, in short) provide a new formal bio-inspired modelling framework, which has been successfully used for modelling population dynamics on real ecosystems. The semantics of these systems is captured by the Direct distribution based on Consistent Blocks Algorithm (DCBA), which has been engineered into software simulation tools. In particular, MeCoSim (Membrane Computing Simulator) is a GUI developed in the framework of P-Lingua that can be used as a simulation environment for running virtual experiments. The parameters of each scenario to be simulated can be easily adjusted in a visual way, as well as the settings for the desired output format, thus facilitating the validation of the designed models against real data. The simulation of PDP systems is data intensive for large models. Therefore, the development of efficient simulators for this field is needed. In fact, the computational power of GPUs is currently being used to accelerate simulations of PDP systems. We illustrate the modelling framework presented with a case study concerning pandemics.

Solving sudoku with Membrane Computing

Sudoku is a very popular puzzle which consists on placing several numbers in a squared grid according to some simple rules. In this paper we present an efficient family of P systems which solve sudokus of any order verifying a specific property. The solution is searched by using a simple human-style method. If the sudoku cannot be solved by using this strategy, the P system detects this drawback and then the computations stops and returns No. Otherwise, the P system encodes the solution and returns Yes in the last computation step.

Implementing Enzymatic Numerical P Systems for AI Applications by Means of Graphic Processing Units

A P system represents a distributed and parallel computing model in which basic data structures are, for instance, multisets and strings. Enzymatic Numerical P Systems (ENPS) are a type of P systems whose basic data structures are sets of numerical variables. Separately, GPGPU (general-purpose computing on graphics processing units) is a novel technological paradigm which focuses on the development of tools for graphic cards to solve general purpose problems. This paper proposes an ENPS simulator based on GPUs and presents general concepts about its design and some future ideas and perspectives.