One-Membrane P Systems with Activation and Blocking of Rules

We introduce new possibilities to control the application of rules based on the preceding applications, which can be de ned in a general way for (hierarchical) P systems and the main known derivation modes. Computational completeness can be obtained even for one-membrane P systems with non-cooperative rules and using both activation and blocking of rules, especially for the set modes of derivation. When we allow the application of rules to in uence the application of rules in previous derivation steps, applying a non-conservative semantics for what we consider to be a derivation step, we can even \go beyond Turing"

P Systems: from Anti-Matter to Anti-Rules

The concept of a matter object being annihilated when meeting its corresponding anti-matter object is taken over for rule labels as objects and anti-rule labels as the corresponding annihilation counterpart in P systems. In the presence of a corresponding anti-rule object, annihilation of a rule object happens before the rule that the rule object represents, can be applied. Applying a rule consumes the corresponding rule object, but may also produce new rule objects as well as anti-rule objects, too. Computational completeness in this setting then can be obtained in a one-membrane P system with non-cooperative rules and rule / anti-rule annihilation rules when using one of the standard maximally parallel derivation modes as well as any of the maximally parallel set derivation modes (i.e., non-extendable (multi)sets of rules, (multi)sets with maximal number of rules, (multi)sets of rules a ecting the maximal number of objects). When using the sequential derivation mode, at least the computational power of partially blind register machines is obtained

Complexity of Simulating R Systems by P Systems

We show multiple ways to simulate R systems by non-cooperative P systems with atomic control by promoters and/or inhibitors, or with matter-antimatter annihi- lation rules, with a slowdown by a factor of constant. The descriptional complexity is also linear with respect to that of simulated R system. All these constants depend on how general the model of R systems is, as well as on the chosen control ingredients of P systems. Special attention is paid to the di erences in the mode of rule application in these models

Solving SAT with Antimatter in Membrane Computing

The set of NP-complete problems is split into weakly and strongly NP- complete ones. The di erence consists in the in uence of the encoding scheme of the input. In the case of weakly NP-complete problems, the intractability depends on the encoding scheme, whereas in the case of strongly NP-complete problems the problem is intractable even if all data are encoded in a unary way. The reference for strongly NP-complete problems is the Satis ability Problem (the SAT problem). In this paper, we provide a uniform family of P systems with active membranes which solves SAT { without polarizations, without dissolution, with division for elementary membranes and with matter/antimatter annihilation. To the best of our knowledge, it is the rst solution to a strongly NP-complete problem in this P system model.Ministerio de Economía y Competitividad TIN2012-3743

Solving the Bin-Packing Problem by Means of Tissue P System with 2-Division

The ability of tissue P systems with 2-division for solving NP problems in polynomial time is well-known and many solutions can be found in the literature to several of such problems. Nonetheless, there are very few papers devoted to the Bin-packing problem. The reason may be the difficulties for dealing with different number of bins, capacity and number of objects by using exclusively division rules that produce two offsprings in each application. In this paper we present the design of a family of tissue P systems with 2 division which solves the Bin-packing problem in polynomial time by combining design techniques which can be useful for further research

Proceedings of the Workshop on Membrane Computing, WMC 2016.

yesThis Workshop on Membrane Computing, at the Conference of Unconventional Computation and Natural Computation (UCNC), 12th July 2016, Manchester, UK, is the second event of this type after the Workshop at UCNC 2015 in Auckland, New Zealand*. Following the tradition of the 2015 Workshop the Proceedings are published as technical report. The Workshop consisted of one invited talk and six contributed presentations (three full papers and three extended abstracts) covering a broad spectrum of topics in Membrane Computing, from computational and complexity theory to formal verification, simulation and applications in robotics. All these papers – see below, but the last extended abstract, are included in this volume. The invited talk given by Rudolf Freund, “P SystemsWorking in Set Modes”, presented a general overview on basic topics in the theory of Membrane Computing as well as new developments and future research directions in this area. Radu Nicolescu in “Distributed and Parallel Dynamic Programming Algorithms Modelled on cP Systems” presented an interesting dynamic programming algorithm in a distributed and parallel setting based on P systems enriched with adequate data structure and programming concepts representation. Omar Belingheri, Antonio E. Porreca and Claudio Zandron showed in “P Systems with Hybrid Sets” that P systems with negative multiplicities of objects are less powerful than Turing machines. Artiom Alhazov, Rudolf Freund and Sergiu Ivanov presented in “Extended Spiking Neural P Systems with States” new results regading the newly introduced topic of spiking neural P systems where states are considered. “Selection Criteria for Statistical Model Checker”, by Mehmet E. Bakir and Mike Stannett, presented some early experiments in selecting adequate statistical model checkers for biological systems modelled with P systems. In “Towards Agent-Based Simulation of Kernel P Systems using FLAME and FLAME GPU”, Raluca Lefticaru, Luis F. Macías-Ramos, Ionuţ M. Niculescu, Laurenţiu Mierlă presented some of the advatages of implementing kernel P systems simulations in FLAME. Andrei G. Florea and Cătălin Buiu, in “An Efficient Implementation and Integration of a P Colony Simulator for Swarm Robotics Applications" presented an interesting and efficient implementation based on P colonies for swarms of Kilobot robots. *http://ucnc15.wordpress.fos.auckland.ac.nz/workshop-on-membrane-computingwmc- at-the-conference-on-unconventional-computation-natural-computation

A Characterization of PSPACE with Antimatter and Membrane Creation

The use of negative information provides a new tool for exploring the limits of P systems as computational devices. In this paper we prove that the combination of antimatter and annihilation rules (based on the annihilation of physical particles and antiparticles) and membrane creation (based on autopoiesis) provides a P system model able to solve PSPACE-complete problems. Namely, we provide a uniform family of P system in such P system model which solves the satis ability problem for quanti ed Boolean formulas (QSAT). In the second part of the paper, we prove that all the decision problems which can be solved with this P system model belong to the complexity class PSPACE, so this P system model characterises PSPACE.Ministerio de Economía y Competitividad TIN2012-3743

Automatic Selection of Statistical Model Checkers for Analysis of Biological Models

Statistical Model Checking (SMC) blends the speed of simulation with the rigorous analytical capabilities of model checking, and its success has prompted researchers to implement a number of SMC tools whose availability provides flexibility and fine-tuned control over model analysis. However, each tool has its own practical limitations, and different tools have different requirements and performance characteristics. The performance of different tools may also depend on the specific features of the input model or the type of query to be verified. Consequently, choosing the most suitable tool for verifying any given model requires a significant degree of experience, and in most cases, it is challenging to predict the right one. The aim of our research has been to simplify the model checking process for researchers in biological systems modelling by simplifying and rationalising the model selection process. This has been achieved through delivery of the various key contributions listed below. • We have developed a software component for verification of kernel P (kP) system models, using the NuSMV model checker. We integrated it into a larger software platform (www.kpworkbench.org). • We surveyed five popular SMC tools, comparing their modelling languages, external dependencies, expressibility of specification languages, and performance. To best of our knowledge, this is the first known attempt to categorise the performance of SMC tools based on the commonly used property specifications (property patterns) for model checking. • We have proposed a set of model features which can be used for predicting the fastest SMC for biological model verification, and have shown, moreover, that the proposed features both reduce computation time and increase predictive power. • We used machine learning algorithms for predicting the fastest SMC tool for verification of biological models, and have shown that this approach can successfully predict the fastest SMC tool with over 90% accuracy. • We have developed a software tool, SMC Predictor, that predicts the fastest SMC tool for a given model and property query, and have made this freely available to the wider research community (www.smcpredictor.com). Our results show that using our methodology can generate significant savings in the amount of time and resources required for model verification

P Systems with Randomized Right-hand Sides of Rules

P systems are a model of hierarchically compartmentalized multiset rewriting. We introduce a novel kind of P systems in which rules are dynamically constructed in each step by non-deterministic pairing of left-hand and right-hand sides. We de ne three variants of right-hand side randomization and compare each of them with the power of conventional P systems. It turns out that all three variants enable non-cooperative P systems to generate exponential (and thus non-semi-linear) number languages. We also give a binary normal form for one of the variants of P systems with randomized rule right-hand sides. Finally, we also discuss extensions of the three variants to tissue P systems, i.e., P systems on an arbitrary graph structure