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Spiking neural P systems (SN P systems) refer to a type of distributed parallel neural computation model in the membrane computing framework. In this work, a new type of SN P system is examined, i.e., spiking neural P systems with polarizations (PSN P systems), by taking inspiration from the polarized cell membrane of a neuron. For the PSN P system, the firing condition of rules is the neuron-associated polarization. This work focuses on examining the computation power of the PSN P systems adopting the sequential mode induced by the minimum spike number, where at a computation step, one (resp., all) of neurons that hold the minimum spike number within the neurons which can fire will fire, i.e., the min-sequentiality strategy (resp., min-pseudo-sequentiality strategy). We prove that PSN P systems adopting the min-sequentiality strategy or min-pseudo-sequentiality strategy are Turing universal as the number generating devices. These results indicate the computation power of PSN P systems is robust regarding their strategies of sequentiality.

Spiking neural P systems (SN P systems) are a class of parallel computing models, inspired by the way in which neurons process information and communicate to each other by means of spikes. In this work, we consider a variant of SN P systems, SN P systems with polarizations (PSN P systems), where the integrate-and-fire conditions are associated with polarizations of neurons. The computation power of PSN P systems working in the asynchronous mode (at a computation step, a neuron with enabled rules does not obligatorily fire), instead of the synchronous mode (a neuron with enabled rules should fire), is investigated. We proved that asynchronous PSN P systems with extended rules (the application of a rule can produce more than one spikes) or standard rules (all rules can only produce a spike) can both characterize partially blind counter machines, hence, such systems are not Turing universal. The equivalence of the computation power of asynchronous PSN P systems in both cases of using extended rules or standard rules indicates that asynchronous PSN P systems are robust in terms of the amount of information exchanged among neurons. It is known that synchronous PSN P systems with standard rules are Turing universal, so these results also suggest that the working model, synchronization or asynchronization, is an essential ingredient for a PSN P system to achieve a powerful computation capability.

In the majority of models of P systems, rules are applied at the ticks of a global clock and their products are introduced into the system for the following step. In timed P systems, di erent integer durations are statically assigned to rules; time-free P systems are P systems yielding the same languages independently of these durations. In clock-free P systems, durations are real and are assigned to individual rule applications; thus, different applications of the same rule may last for a different amount of time. In this paper, we formalise timed, time-free, and clock-free P system within a framework for generalised parallel rewriting. We then explore the relationship between these variants of semantics. We show that clock-free P systems cannot effi ciently solve intractable problems. Moreover, we consider un-timed systems where we collect the results using arbitrary timing functions as well as un-clocked P systems where we take the union over all possible per-instance rule durations. Finally, we also introduce and study mode-free P systems, whose results do not depend on the choice of a mode within a fixed family of modes, and compare mode-freeness with clock-freeness.

Contextual array P systems generate two-dimensional picture arrays by sequential application of contextual array rules on picture arrays. Here we consider a parallel mode of application of the contextual array rules in the regions of such an array P system. We call the resulting array P system as parallel contextual array P system (PCAP) and we study the generative power of these systems. A main advantage is that the number of membranes needed in the constructions of the PCAP for picture array generation is reduced in comparison with the sequential counterpart.

Numerical P systems (NP systems) are a class of computing models inspired both from the structure of living cells and the economic reality. Enzymatic numerical P systems (ENP systems) are a variant of NP systems, which were successfully applied in autonomous robot control. In this work, we investigate the computational power of ENP systems working in the sequential mode: both non-deterministically sequential and deterministically sequential systems are considered. For non-deterministically sequential ENP systems, we improve a known universality result by reducing the number of membranes and programs used in the universal system from 7 to 2 and 19 to 8, respectively. For deterministically sequential ENP systems, we prove that they are Turing universal even when linear production functions are used and each function has at most two variables. As a byproduct, we obtain a small deterministic universal enzymatic numerical P system with 23 membranes and 118 programs by simulating a specific universal register machine. These results give a positive answer to an open problem formulated in Gh. Paun (2014) [37] .

In search of the borderline between universality and non-universality of purely catalytic P systems, we prove that purely catalytic P systems with one catalyst are universal when inhibitors plus promoters or a priority relation among the rules plus promoters are used. The universality is also obtained for purely catalytic cooperating P systems with two catalysts (without any control on the use of rules). Some related open problems are also formulated.

In spite of the fact that many ways of using the evolution rules in a P system were already investigated, there is still a case, which we call the flat maximal parallelism, which appeared in several papers, but which deserves a more careful attention: in each step, in each membrane, a maximal set of applicable rules is chosen and each rule in the set is applied exactly once. In this work, flat maximal parallelism is studied for non-cooperating P systems with promoters. Specifically, we prove that non-cooperating P systems with at most one promoter associated with any rule, working in the flat maximally parallel way, are Turing universal (the Turing universality of such P systems is open if they work in the maximally parallel way). Moreover, a uniform solution to the SAT problem is provided by using non-cooperating P systems with promoters and membrane division, working in the flat maximal parallel way.

A control strategy on the computations in a one-catalyst P system is provided: the rules are assumed "colored" and in each step only rules of the same "color" are used. Such control leads to Turing universality for one-catalyst P systems with one membrane. Turing universality is also reached for purely catalytic P systems with two catalysts, and for purely catalytic P systems with only one catalyst and cooperating rules working in the so-called terminal mode.

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons process information and communicate by means of spikes, where neurons are different in the sense that they can have different sets of spiking rules. In this work, we consider a variant of SN P systems with two restrictions: (1) all neurons contain only spikes (neurons are homogeneous in this case, since they can be considered as containers of spikes), while the spiking rules are moved on the synapses; (2) all synapses are homogeneous in the sense that each synapse has the same set of rules. These restrictions correspond to the fact that the SN P system consists of only one kind of neurons and one kind of synapses. The computational power of SN P systems with homogeneous neurons and synapses is investigated. Specifically, it is proved that such systems are Turing universal as both number generating and number accepting devices.

Spiking neural P systems with rules on synapses are a new variant of spiking neural P systems. In the systems, the neuron contains only spikes, while the spiking/forgetting rules are moved on the synapses. It was obtained that such system with 30 neurons (using extended spiking rules) or with 39 neurons (using standard spiking rules) is Turing universal. In this work, this number is improved to 6. Specifically, we construct a Turing universal spiking neural P system with rules on synapses having 6 neurons, which can generate any set of Turing computable natural numbers. As well, it is obtained that spiking neural P system with rules on synapses having less than two neurons are not Turing universal: i) such systems having one neuron can characterize the family of finite sets of natural numbers; ii) the family of sets of numbers generated by the systems having two neurons is included in the family of semi-linear sets of natural numbers.

Axon P systems are computing models with a linear structure in the sense that all nodes (i.e., computing units) are arranged one by one along the axon. Such models have a good biological motivation: an axon in a nervous system is a complex information processor of impulse signals. Because the structure of axon P systems is linear, the computational power of such systems has been proved to be greatly restricted; in particular, axon P systems are not universal as language generators. It remains open whether axon P systems are universal as number generators. In this paper, we prove that axon P systems are universal as both number generators and function computing devices, and investigate the number of nodes needed to construct a universal axon P system. It is proved that four nodes (respectively, nine nodes) are enough for axon P systems to achieve universality as number generators (respectively, function computing devices). These results illustrate that the simple linear structure is enough for axon P systems to achieve a desired computational power.

A variety of two-dimensional array grammar models generating picture array languages have been introduced and investigated, utilizing and extending the well-established notions and techniques of formal string language theory. On the other hand the versatile computing model with a generic name of P system in the area of membrane computing, has turned out to be a rich framework for different kinds of problems in a variety of fields. Picture array generation in the field of two-dimensional (2D) languages is one such area where P systems with array objects and array rewriting, referred to as array P systems, have been fruitfully employed in increasing the generating power of the 2D grammar models. A variety of array P systems have been proposed in the literature. The objective of this survey is to review and describe the salient features of the major types of array P systems, which have served as the basis for developing other kinds of array P systems. Applications of these array P systems are also briefly described besides indicating possible new directions of investigation.

Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.

Inspired by mitosis process and membrane fission processes, cell-like P systems with symport/antiport rules and membrane division rules or membrane separation rules have been introduced, respectively. These computation systems have two key features: the ability to have infinite copies of some objects (within an active environment) and to generate an exponential workspace in polynomial time. In this work, we extend the P-Lingua framework for simulating that kind of P systems taking into account these two features. Consequently, a new simulator has been developed and included in pLinguaCore library. The functioning of the simulator has been checked by simulating efficient solutions to SAT problem using a family of cell-like P systems with symport/antiport rules and membrane division rules or membrane separation rules. The corresponding MeCoSim based application is also provided. ∗Address for correspondence: Huazhong University of Science and Technology, Wuhan 430074, Hubei, China. Received February 2015; revised April 2015. 212 L.F. Macias-Ramos et al. / P–Lingua Simulator for P Systems with Symport/Antiport Rules

P systems are a class of computational models inspired by the structure and the functioning of a living cell. In the semantics of P systems, there exists a global clock, which marks the time for the system, and the execution time of each rule takes exactly one time unit. However, in living cells, the execution time of different biochemical reactions is dependent on many uncontrollable factors, and it is hard to know precisely the specific execution time of a reaction. In this work, with this biological inspiration, we consider the class of P systems with active membranes that are "robust" against the execution time of rules. Specifically, we give a time-free uniform solution to the SAT problem using P systems with active membranes, where the constructed P system can solve a family of instances with an arbitrarily given size, and the execution time of the involved rules has no influence on the correctness of the solution. We also prove that any Turing computable set of numbers can be generated by a time-free P system with active membranes in the sense that the set of numbers generated by the given P system with active membranes does not depend on the execution time of rules.

P systems are a class of distributed and parallel computing models inspired by the structure and the functioning of a single cell and complexes of cells. The computational efficiency of P systems with active membranes has been investigated widely with the assumption that the application of rules is completed in exactly one time unit. However, in biological facts, different biological processes may take different times to complete, and the execution time of certain biological process could vary because of external uncontrollable conditions. With this biological motivation, in this work, we solve SAT problem by a family of P systems with active membranes in a time-free manner in the sense that the correctness of the solution does not depend on the precise timing of the involved rules. In such a solution, standard cell division rules for elementary membranes are applied: the newly generated membranes have the same label with their parent membrane. This result answers an open problem formulated in Song et al. (Theor Comput Sci 529:61---68, 2014).

Spiking neural P (SN P) systems are a class of parallel computation models inspired by neurons, where the firing condition of a neuron is described by a regular expression associated with spiking rules. However, it is NP-complete to decide whether the number of spikes is in the length set of the language associated with the regular expression. In this paper, in order to avoid using regular expressions, two major and rather natural modifications in their form and functioning are proposed: the spiking rules no longer check the number of spikes in a neuron, but, in exchange, a polarization is associated with neurons and rules, one of the three electrical charges −, 0,+. Surprisingly enough, the computing devices obtained are still computationally complete, which are able to compute all Turing computable sets of natural numbers. On this basis, the number of neurons in a universal SN P system with polarizations is estimated. Several research directions are mentioned at the end of this paper.

Labelled splicing P systems are distributed parallel computing models, where sets of strings that evolve by splicing rules are labelled. In this work, we consider labelled splicing systems with the following modifications: (i) The strings in the membranes are present in arbitrary number of copies; (ii) the rules in the regions are finite in number. Results on the language family generated by the labelled splicing system in comparison with the language families of the Chomsky hierarchy, including recursively enumerable languages, are obtained, by involving only either one or two membranes in the P systems considered.

Rewriting P systems, as language generating devices, are one of the earliest classes of P systems with structured strings as objects and the rewriting rules as evolution rules. Flat splicing is an operation on strings, inspired by a splicing operation on circular strings. In this work, we consider a variant of rewriting P systems with only regular or linear rewriting rules and alphabetic flat splicing rules, and the language generative power of rewriting P systems with flat splicing rules in comparison with flat splicing systems and Chomsky hierarchy is investigated.

Tissue P systems are a class of distributed and parallel models of computation inspired by the way of communication among living cells or between cells and their environment. In this work, we investigate the computational power of tissue P systems, where each rule is assigned either with a label chosen from an alphabet or with the empty label λ . The sequence of labels of rules applied during a halting computation is defined as the result of the computation, and the set of all results computed by a given tissue P system is called a control language. We prove that tissue P systems with antiport rules of weight one and without symport rules characterize regular languages; tissue P systems with antiport rules of weight at most two (resp., symport rules of weight at most two) without symport rules (resp., antiport rules) are universal. Tissue P systems with antiport rules of weight one and symport rules of weight one are also proved to be universal. These results show that the rule complexity is crucial for tissue P systems to achieve a desired computational power.

Spiking neural P systems (SN P systems, for short) are a class of membrane systems inspired from the way the neurons process information and communicate by means of spikes. In this paper, we introduce and investigate a new class of SN P systems, with spiking rules placed on synapses. The computational completeness is first proved, then two small universal SN P systems with rules on synapses for computing functions are constructed. Specifically, when using standard spiking rules, we obtain a universal system with 39 neurons, while when using extended spiking rules on synapses, a universal SN P system with 30 neurons is constructed.

P systems are a class of distributed and parallel computation models inspired by the structure and the functioning of living cells. P systems have been used to solve computation hard problems, where the execution of each rule is completed in unit time (a global clock is assumed for timing and synchronizing the execution of rules). The assumption that the execution of each rule takes exactly one time unit plays an vital role to make a system working synchronously, and it has also been used to characterize the computational efficiency and time complexity of a system. In this work, we investigate the computation power of P systems without such time assumption. Specifically, we give a time-free solution to SAT problem using P systems with active membranes in the sense that the correctness of the solution does not depend on the precise timing of the involved rules.

The biologically inspired model, known as P system, has proved to be a general framework for investigating several problems related to computing in different fields. Two-dimensional picture array languages is one such area in which different kinds of P systems have been constructed for picture array generation. Incorporating the feature of permitting symbols in the rules, a new variety of array P system is constructed here for generating picture languages consisting of picture arrays. The advantage of this approach is that there is a reduction in the number of membranes used in the construction, in comparison to the existing array P system models.

Splicing P systems are a class of distributed parallel computing models in the framework of membrane computing, which are inspired by the recombination of DNA molecules under the influence of restriction enzymes. In this work, a variant of P machine, called splicing P machine, is presented, which can provide an automata approach to investigate splicing P systems. It is shown that such a device can do what the splicing P system does. Several examples are given to illustrate that splicing P machine can provide the same results as the corresponding splicing P system. This work provides an answer to an open problem formulated by G. Ciobaun and M. Gontineac.

Tissue P systems are a class of distributed parallel computing devices inspired by biochemical interactions between cells in a tissue-like arrangement, where objects can be exchanged by means of communication channels. In this work, inspired by the biological facts that the movement of most objects through communication channels is controlled by proteins and proteins can move through lipid bilayers between cells (if these cells are fused), we present a new class of variant tissue P systems, called tissue P systems with protein on cells, where multisets of objects (maybe empty), together with proteins between cells are exchanged. The computational power of such P systems is studied. Specifically, an efficient (uniform) solution to the SAT problem by using such P systems with cell division is presented. We also prove that any Turing computable set of numbers can be generated by a tissue P system with protein on cells. Both of these two results are obtained by such P systems with communication rules of length at most 4 (the length of a communication rule is the total number of objects and proteins involved in that rule). Ministerio de Economía y Competitividad TIN2012-37434

The notion of time-free solution to decision problem by P systems with proteins on membranes, in the sense that the correctness of the solution is irrelevant to the times associated with the involved rules, is defined.A time-free uniform solution to the SAT problem by P systems with proteins on membranes is given. P systems with proteins on membranes are a class of bio-inspired computing models, where the execution of each rule completes in exactly one time unit. However, in living cells, the execution time of biochemical reactions is difficult to know precisely because of various uncontrollable factors. In this work, we present a time-free uniform solution to SAT problem by P systems with proteins on membranes in the sense that the correctness of the solution is irrelevant to the times associated with the involved rules, and the P systems are constructed from the sizes of instances.

Cell-like P systems with symport/antiport rules are inspired by the structure of a cell and the way of communicating substances through membrane channels between neighboring regions. In this work, channel states are introduced into cell-like P systems with symport/antiport rules, and we call this variant of communication P systems as cell-like P systems with channel states and symport/antiport rules. In such P systems, at most one channel is established between neighboring regions, each channel associates with one state in order to control communication at each step, and rules are used in a sequential manner: on each channel at most one rule can be used at each step. The computational power of such P systems is investigated. Specifically, we show that cell-like P systems with two states and using uniport rules, or with any number of states and using antiport rules of length two, are able to compute only finite sets of non-negative integers.We further prove that cell-like P systems with two membranes are as powerful as Turing machines when channel states and symport/antiport rules are suitably combined. The results show that channel states are a feature that can increase the computational power of cell-like P systems with symport/antiport rules. Ministerio de Economía y Competitividad TIN2012-37434

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses, where there is a synapse between each pair of connected neurons. However, in a biological system, there can be several synapses for each pair of connected neurons. In this study, inspired by this biological observation, synapses in a spiking neural P system are endowed with integer weight denoting the number of synapses for each pair of connected neurons. With the price of weight on synapses, quite small universal spiking neural P systems can be constructed. Specifically, a universal spiking neural P system with standard rules and weight having 38 neurons is produced as device of computing functions; as generator of sets of numbers, we find a universal system with standard rules and weight having 36 neurons.

Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons. In this work, the features of neuron division and neuron budding are introduced into the framework of spiking neural P systems, which are processes inspired by neural stem cell division. With neuron division and neuron budding, a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes. In this way, spiking neural P systems can efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with an efficient solution to SAT problem.

Different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, a realistic definition of nonsynchronized spiking neural P systems (SN P systems, for short) is considered: during the work of an SN P system, the execution times of spiking rules cannot be known exactly (i.e., they are arbitrary). In order to establish robust systems against the environmental factors, a special class of SN P systems, called time-free SN P systems, is introduced, which always produce the same computation result independent of the execution times of the rules. The universality of time-free SN P systems is investigated. It is proved that these P systems with extended rules (several spikes can be produced by a rule) are equivalent to register machines. However, if the number of spikes present in the system is bounded, then the power of time-free SN P systems falls, and in this case, a characterization of semilinear sets of natural numbers is obtained.

In a biological system, if a long enough time interval is given, an enabled chemical reaction will finish its reaction in the given time interval. With this motivation, it is natural to impose a bound on the time interval when an enabled spiking rule in a spiking neural P system (SN P system, for short) remains unused. In this work, a new working mode of SN P systems is defined, which is called limited asynchronous mode. In an SN P system working in limited asynchronous mode, if a rule is enabled at some step, this rule is not obligatorily used. From this step on, if the unused rule may be used later, it should be used in the given time interval. If further spikes make the rule non-applicable, then the computation continues in the new circumstances. The computation result of a computation in an SN P system working in limited asynchronous mode is defined as the total number of spikes sent into the environment by the system. It is proved that limited asynchronous SN P systems with standard spiking rules are universal. If the number of spikes present in each neuron of a limited asynchronous SN P system with standard spiking rules is bounded during a computation, then the power of a limited asynchronous SN P system with standard spiking rules falls drastically, and we get a characterization of semilinear sets of numbers.

A tissue P system with cell division is a computing model which has two basic features: intercellular communication and the ability of cell division. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to computationally hard problems in polynomial time. In this work we present an efficient solution to the tripartite matching problem by a family of such devices. This solution leads to an interesting open problem whether tissue P systems with cell division and communication rules of length 2 can solve NP-complete problems. An answer to this open problem will provide a borderline between efficiency and non-efficiency in terms of the lengths of communication rules Ministerio de Educación y Ciencia TIN2009-13192 Junta de Andalucía P08-TIC-04200

Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic was also investigated in the framework of spiking neural P systems. One of the small universality results is that a small weakly universal extended spiking neural P system with 12 neurons was constructed. In this paper, a new way is introduced for simulating register machines by spiking neural P systems, where only one neuron is used for all instructions of the register machine; in this way, we can use less neurons to construct universal spiking neural P system. Specifically, we give a smaller weakly universal spiking neural P system that uses extended rules and has only 9 neurons.

Syntactic models constitute one of the main areas of mathematical studies on picture array generation. A number of 2D grammar models have been proposed motivated by a variety of applications. A P system is a biologically motivated new computing model, proposed by Paun in the area of membrane computing. It is a rich framework for dealing with different problems, including the problem of handling picture array generation. In this paper, the generative power of the array-rewriting P system with pure 2D context-free rules is investigated by comparing it with other 2D grammar models, thus bringing out the suitability of this P system model for picture array generation.

Spiking neural P systems are a class of distributed parallel computing models inspired from the way the neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we consider a restricted variant of spiking neural P systems, called homogeneous spiking neural P systems, where each neuron has the same set of rules. The universality of homogeneous spiking neural P systems is investigated. One of universality results is that it is sufficient for homogeneous spiking neural P system to have only one neuron that behaves nondeterministically in order to achieve Turing completeness.

P systems are computing models inspired by some basic features of biological membranes. In this work, membrane division, which provides a way to obtain an exponential workspace in linear time, is introduced into (cell-like) P systems with communication (symport/antiport) rules, where objects are never modified but they just change their places. The computational efficiency of this kind of P systems is studied. Specifically, we present a (uniform) linear time solution to the NP-complete problem, Subset Sum by using division rules for elementary membranes and communication rules of length at most 3. We further prove that such P system allowing division rules for non-elementary membranes can efficiently solve the PSPACE-complete problem, QSAT in a uniform way. Ministerio de Economía y Competitividad TIN2012-37434

P systems are a class of distributed parallel computing models inspired by the structure and the functioning of a living cell, where the execution of each rule is completed in exactly one time unit (a global clock is assumed). How-ever, in living cells, the execution time of different biological processes is difficult to know precisely and very sensi-tive to environmental factors that might be hard to control. Inspired from this biological motivation, in this work, timed polarization P systems with membrane creation are intro-duced and their computational efficiency and universality are investigated. Specifically, we give a time-free semi-uniform solution to the SAT problem by a family of P systems with membrane creation in the sense that the correctness of the solution is irrelevant to the times associated with the involved rules. We also prove that timefree P systems with membrane creation are computationally universal. Ministerio de Economía y Competitividad TIN2012-37434

The P systems (or membrane systems) are a class of distributed parallel computing devices of a biochemical type, where membrane division is the frequently investigated way for obtaining an exponential working space in a linear time, and on this basis solving hard problems, typically NP-complete problems, in polynomial (often, linear) time. In this paper, using another way to obtain exponential working space --- membrane separation, it was shown that Satisfiability Problem and Hamiltonian Path Problem can be deterministically solved in linear or polynomial time by a uniform family of P systems with separation rules, where separation rules are not changing labels, but polarizations are used. Some related open problems are mentioned.

This paper addresses the problem of removing the polarization of membranes from P systems with active membranes - and this is achieved by allowing the change of membrane labels by means of communication rules or by membrane dividing rules. As consequences of these results, we obtain the universality of P systems with active membranes which are allowed to change the labels of membranes, but do not use polarizations. Universality results are easily obtained also by direct proofs. By direct constructions, we also prove that SAT can be solved in linear time by systems without polarizations and with label changing possibilities. If non-elementary membranes can be divided, then SAT can be solved in linear time without using polarizations and label changing. Several open problems are also formulated.

The P systems are a class of distributed parallel computing devices of a biochemical type. In this note, we show that by using membrane separation to obtain exponential workspace, SAT problem can be solved in linear time in a uniform and con°uent way by active P systems without polarizations. This improves some results already obtained by A. Alhazov, Ts. Ishdorj. A universality result related to membrane separation is also obtained.

Spiking neural P systems with weights (WSN P systems, for short) are a new variant of spiking neural P systems, where the rules of a neuron are enabled when the potential of that neuron equals a given value. It is known that WSN P systems are universal by simulating register machines. However, in these universal systems, no bound is considered on the number of neurons and rules. In this work, a restricted variant of WSN P systems is considered, called simple WSN P systems, where each neuron has only one rule. The complexity parameter, the number of neurons, to construct a universal simple WSN P system is investigated. It is proved that there is a universal simple WSN P system with 48 neurons for computing functions; as generator of sets of numbers, there is an almost simple (that is, each neuron has only one rule except that one neuron has two rules) and universal WSN P system with 45 neurons. Ministerio de Ciencia e Innovación TIN2009-13192 Junta de Andalucía P08-TIC04200

Gene regulatory networks are useful models for biologists to understand the interactions among genes in living organisms. In this work, we consider a special class of gene regulatory networks, called logical networks, where the “links” among genes are inferred by logic analysis of phylogenetic profiles. We provide a formalization of logic networks, and introduce a membrane computing model for reconstructing logical networks and reproducing their dynamics. An example logical network is simulated by using software P-Lingua and MeCoSim. The simulation result shows that membrane computing is a versatile framework for modeling different biological scenarios. Ministerio de Ciencia e Innovación TIN2012-37434 Junta de Andalucía P08-TIC-04200

In the area of membrane computing, the biologically inspired model known as \(P\) system has proved to be a rich framework for studying several types of problems. Picture array generation is one such problem for which different \(P\) systems have been constructed in the literature. Incorporating the feature of permitting symbols in the rules, array \(P\) systems are constructed here for generating picture languages consisting of picture arrays. The advantage of this approach is that there is a reduction in the number of membranes used in the construction, in comparison to the existing array \(P\) system model.

Spiking neural P systems are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this paper, the problem of looking for small universal computing devices is investigated in the framework of spiking neural P systems. A new approach is introduced to simulate register machines by spiking neural P systems, where only one neuron is used for all instructions of the simulated register machine; in this way, less neurons are used to construct universal spiking neural P systems working in exhaustive mode. Specifically, a universal spiking neural P system with 36 neurons is constructed, which works in exhaustive mode. This significantly improves the already known result, where 125 neurons are used.

Ramsey numbers deal with conditions when a combinatorial object necessarily contains some smaller given objects. It is well known that it is very difficult to obtain the values of Ramsey numbers. In this work, a theoretical chemical/biological solution is presented in terms of membrane computing for the decision version of Ramsey number problem, that is, to decide whether an integer n is the value of Ramsey number R(k, l), where k and l are integers. Junta de Andalucía P08–TIC-04200 Ministerio de Ciencia e Innovación TIN2009–13192

Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic started to be considered also in the framework of (synchronized) spiking neural P systems. In this work, it is focused on small universal spiking neural P systems working in a non-synchronized manner. Specifically, it is proved that there is an asynchronous spiking neural P system with 76 neurons that is equivalent to a universal register machine for computing functions. As generator of sets of numbers, a universal asynchronous spiking neural P system with 75 neurons is constructed.

Besides usual spikes employed in spiking neural P systems, we consider "anti-spikes", which participate in spiking and forgetting rules, but also annihilate spikes when meeting in the same neuron. This simple extension of spiking neural P systems is shown to considerably simplify the universality proofs in this area: all rules become of the form bc ! b0 or bc ! ¸, where b; b0 are spikes or anti-spikes. Therefore, the regular expressions which control the spiking are the simplest possible, identifying only a singleton. A possible variation is not to produce anti-spikes in neurons, but to consider some "inhibitory synapses", which transform the spikes which pass along them into anti- spikes. Also in this case, universality is rather easy to obtain, with rules of the above simple forms. Junta de Andalucía P08 – TIC 04200

A current research topic in membrane computing is to find more realistic P systems from a biological point of view, and one target in this respect is to relax the condition of using the rules in a maximally parallel way. We contribute in this paper to this issue by considering the minimal parallelism of using the rules: if at least a rule from a set of rules associated with a membrane or a region can be used, then at least one rule from that membrane or region must be used, without any other restriction (e.g., more rules can be used, but we do not care how many). Weak as it might look, this minimal parallelism still leads to universality. We first prove this for the case of symport/antiport rules. The result is obtained both for generating and accepting P systems, in the latter case also for systems working deterministically. Then, we consider P systems with active membranes, and again the usual results are obtained: universality and the possibility to solve NP-complete problems in polynomial time (by trading space for time).

In this note, we consider the problem of looking for small universal one-symbol tissue P systems with symport/antiport rules. It is proved that six cells suffice to generate any recursively enumerable set of natural numbers by such a onesymbol tissue P system with symport/antiport rules, under the restriction that only one channel is allowed between two cells or between a cell and the environment. As for the case of allowing two channels between a cell and the environment, it is shown that the computational completeness can be obtained by one-symbol tissue P systems with symport/antiport rules having at most five cells. These results partially answer an open problem formulated by Artiom Alhazov, Rudolf Freund and Marion Oswald.