A P-Lingua based simulator for tissue P systems

AbstractInvestigations within the field of tissue-like P systems are being conducted, on one hand studying their computational efficiency, and on the other hand exploring the possibilities to use them as a computational modelling framework to biological phenomena.In both cases it is necessary to develop software that provides simulation tools (simulators) for the existing variety of tissue P systems. Such simulators allow us to carry on computations of solutions to computationally hard problems on certain (small) instances. Moreover, they also provide a way to verify tissue-like models for real biological processes, by means of experimental data.The paper presents an extension of P-Lingua (a specification language intended to become a standard for software devoted to P systems), in order to cover the class of tissue-like P systems, that were not considered in the previous release. This extension involves on one hand defining the syntax to be used, and on the other hand introducing a new built-in simulation algorithm that has been added to the core library of P-Lingua

Automatic 2d image segmentation using tissue-like p system

This paper uses P-Lingua, a standard programming language that is designed specifically for P systems, to automatically simulate the region-based segmentation of 2D images. P-Lingua, which is based on membrane computing, links to Java Netbeans using the PLinguaCore4 Java library to automatically codify the pixels of the input image as long as automatically draw the output segmented image. Many methods have been suggested previously and used for artificial image segmentation, but to the best of our knowledge, none of those techniques were automatic, where the image was codified manually and the visualization of the output image was done manually in the tissue simulator which takes time and effort, especially when dealing with large images in the system. Two types of pixel adjacency have been utilized in this paper, namely; 4-adjacency and 8-adjacency. The jacquard index method has been used to measure the accuracy of the segmentation. The results of the proposed method demonstrated that beside its ability to automatically segmenting 2D images with arbitrary sizes, it is more efficient and faster than the tissue simulator tool, since the latter needs the input image to be codified manually pixel by pixel which makes it impractical for real-world applications

A syntax for semantics in P-Lingua

P-Lingua is a software framework for Membrane Computing, it includes a programming language, also called P-Lingua, for writting P system de nitions using a syntax close to standard scienti c notation. The rst line of a P-Lingua le is an unique identi er de ning the variant or model of P system to be used, i.e, the semantics of the P system. Software tools based on P-Lingua use this identi er to select a simulation algorithm implementing the corresponding derivation mode. Derivation modes de ne how to obtain a con guration Ct+1 from a con guration Ct. This information is usually hard-coded in the simulation algorithm. The P system model also de nes what types or rules can be used, the P-Lingua compiler uses the identi er to select an speci c parser for the le. In this case, a set of parsers is codi ed within the compiler tool. One for each unique identi er. P-Lingua has grown during the last 12 years, including more and more P system models. From a software engineering point of view, this approximation implies a continous development of the framework, leading to a monolithic software which is hard to debug and maintain. In this paper, we propose a new software approximation for the framework, including a new syntax for de ning rule patterns and derivation modes. The P-Lingua users can now de ne custom P system models instead of hard-coding them in the software. This approximation leads to a more exible solution which is easier to maintain and debug. Moreover, users could de ne and play with new/experimental P system models

The Computational Complexity of Tissue P Systems with Evolutional Symport/Antiport Rules

Tissue P systems with evolutional communication (symport/antiport) rules are computational models inspired by biochemical systems consisting of multiple individuals living and cooperating in a certain environment, where objects can be modified when moving from one region to another region. In this work, cell separation, inspired from membrane fission process, is introduced in the framework of tissue P systems with evolutional communication rules.The computational complexity of this kind of P systems is investigated. It is proved that only problems in class P can be efficiently solved by tissue P systems with cell separation with evolutional communication rules of length at most (��, 1), for each natural number �� ≥ 1. In the case where that length is upper bounded by (3, 2), a polynomial time solution to the SAT problem is provided, hence, assuming that P ̸= NP a new boundary between tractability and NP-hardness on the basis of the length of evolutional communication rules is provided. Finally, a new simulator for tissue P systems with evolutional communication rules is designed and is used to check the correctness of the solution to the SAT problem

Simulating FRSN P Systems with Real Numbers in P-Lingua on sequential and CUDA platforms

Fuzzy Reasoning Spiking Neural P systems (FRSN P systems, for short) is a variant of Spiking Neural P systems incorporating fuzzy logic elements that make it suitable to model fuzzy diagnosis knowledge and reasoning required for fault diagnosis applications. In this sense, several FRSN P system variants have been proposed, dealing with real numbers, trapezoidal numbers, weights, etc. The model incorporating real numbers was the first introduced [13], presenting promising applications in the field of fault diagnosis of electrical systems. For this variant, a matrix-based algorithm was provided which, when executed on parallel computing platforms, fully exploits the model maximally parallel capacities. In this paper we introduce a P-Lingua framework extension to parse and simulate FRSN P systems with real numbers. Two simulators, implementing a variant of the original matrix-based simulation algorithm, are provided: a sequential one (written in Java), intended to run on traditional CPUs, and a parallel one, intended to run on CUDAenabled devices.Ministerio de Economía y Competitividad TIN2012-3743

Parallel simulation of Population Dynamics P systems: updates and roadmap

Population Dynamics P systems are a type of multienvironment P systems that serve as a formal modeling framework for real ecosystems. The accurate simulation of these probabilisticmodels, e.g. with Direct distribution based on Consistent Blocks Algorithm, entails large run times. Hence, parallel platforms such as GPUs have been employed to speedup the simulation. In 2012, the first GPU simulator of PDP systems was presented. However, it was able to run only randomly generated PDP systems. In this paper, we present current updates made on this simulator, involving an input modu le for binary files and an output module for CSV files. Finally, the simulator has been experimentally validated with a real ecosystem model, and its performance has been tested with two high-end GPUs: Tesla C1060 and K40.Ministerio de Economía y Competitividad TIN2012-37434Junta de Andalucía P08-TIC-0420

A New Strategy to Improve the Performance of PDP-Systems Simulators

One of the major challenges that current P systems simulators have to deal with is to be as efficient as possible. A P system is syntactically described as a membrane structure delimiting regions where multisets of objects evolve by means of evolution rules. According to that, on each computation step, the applicability of the rules for the current P system configuration must be calculated. In this paper we extend previous works that use Rete-based simulation algorithm in order to improve the time consumed during the checking phase in the selection of rules. A new approach is presented, oriented to the acceleration of Population Dynamics P Systems simulations.Ministerio de Economía y Competitividad TIN2012- 3743

Simulation of Rapidly-Exploring Random Trees in Membrane Computing with P-Lingua and Automatic Programming

Methods based on Rapidly-exploring Random Trees (RRTs) have been widely used in robotics to solve motion planning problems. On the other hand, in the membrane computing framework, models based on Enzymatic Numerical P systems (ENPS) have been applied to robot controllers, but today there is a lack of planning algorithms based on membrane computing for robotics. With this motivation, we provide a variant of ENPS called Random Enzymatic Numerical P systems with Proteins and Shared Memory (RENPSM) addressed to implement RRT algorithms and we illustrate it by simulating the bidirectional RRT algorithm. This paper is an extension of [21]a. The software presented in [21] was an ad-hoc simulator, i.e, a tool for simulating computations of one and only one model that has been hard-coded. The main contribution of this paper with respect to [21] is the introduction of a novel solution for membrane computing simulators based on automatic programming. First, we have extended the P-Lingua syntax –a language to define membrane computing models– to write RENPSM models. Second, we have implemented a new parser based on Flex and Bison to read RENPSM models and produce source code in C language for multicore processors with OpenMP. Finally, additional experiments are presented.Ministerio de Economía, Industria y Competitividad TIN2017-89842-

Fast Hardware Implementations of Static P Systems

In this article we present a simulator of non-deterministic static P systems using Field Programmable Gate Array (FPGA) technology. Its major feature is a high performance, achieving a constant processing time for each transition. Our approach is based on representing all possible applications as words of some regular context-free language. Then, using formal power series it is possible to obtain the number of possibilities and select one of them following a uniform distribution, in a fair and non-deterministic way. According to these ideas, we yield an implementation whose results show an important speed-up, with a strong independence from the size of the P system.Ministry of Science and Innovation of the Spanish Government under the project TEC2011-27936 (HIPERSYS)European Regional Development Fund (ERDF)Ministry of Education of Spain (FPU grant AP2009-3625)ANR project SynBioTI

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. In this paper, we extend the general framework in order to include a new case study related to P. Oleracea species. The extension is made by a new variant within the probabilistic approach, called Probabilistic Guarded P systems (in short, PGP systems). We provide a formal definition, a simulation algorithm to capture the dynamics, and a survey of the associated software.Ministerio de Economía y Competitividad TIN2012- 37434Junta de Andalucía P08-TIC-0420