The inappropriate use of antibiotics has precipitated the emergence of multidrug-resistant bacteria, prompting significant interest in antimicrobial peptides (AMPs) as potential alternatives to traditional antibiotics. Given the prohibitive costs and time-consuming nature of biological experiments, computational methods provide an efficient alternative for the development of AMP-based drugs. However, existing computational studies primarily focus on identifying AMPs with antimicrobial activity, lacking a targeted identification of AMPs against specific microbial species. To address this gap, we propose a peptide-microbe association (PMA) prediction framework, termed AEPMA, which is constructed based on an autoevolutionary heterogeneous graph. Within AEPMA, we construct an innovative peptide-microbe-disease network (PMDHAN). Furthermore, we design an autoevolutionary information aggregation mechanism that facilitates the representation learning of the heterogeneous graph. This model automatically aggregates semantic information within the heterogeneous network while thoroughly accounting for the spatiotemporal dependencies and heterogeneous interactions in the PMDHAN. Experiments conducted on one peptide-microbe and three drug-microbe association datasets demonstrate that the performance of AEPMA outperforms five state-of-the-art methods, demonstrating its robust modeling capability and exceptional generalization ability. In addition, this study identifies a novel anti-Staphylococcus aureus peptide and an anti-Escherichia coli peptide, thereby contributing valuable information for the development of antimicrobial drugs and strategies for mitigating antibiotic resistance.

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables.

With the development of computer-aided engineering, various surrogate-assisted evolutionary algorithms (SAEAs) have been developed to solve the involved computationally expensive multiobjective optimization problems (EMOPs). With the increasing complexity of EMOPs, the number of decision variables has increased from tens to hundreds or even thousands. “Curse of Dimensionality” caused by large-scale decision space poses a great challenge to current SAEAs, which require massive function evaluations (FEs). We propose an SAEA with reference vector guided variables selection, namely RVSPSO, for solving large-scale EMOPs. Specifically, the reference vector guided variable selection strategy is proposed to select critical variables for optimization, which associates reference solutions for enhancing convergence and maintaining diversity. Meanwhile, a reference guided particle swarm optimization is proposed as the optimizer, where the velocities of particles are updated according to the directions pointing from the particles to the reference solution. This update strategy aims to accelerate the convergence rate in large-scale decision space. Moreover, radial basis function networks are used as surrogate models for efficient optimization. Experiments are conducted on large-scale EMOPs with up to 2000 decision variables. Experiment results show the proposed RVSPSO can effectively solve large-scale EMOPs to obtain well-converged and diverse solutions with limited FEs, compared with five state-of-the-art SAEAs.

Tissue P systems with evolutional symport/antiport rules are a variant of tissue P systems, where objects are communicated through regions by symport/antiport rules, and objects may evolve during this process. It is known that such systems are able to solve NP problems in a polynomial time (and exponential space), when cell division is allowed. In this work, we continue to investigate the computational complexity aspects for tissue P systems with evolutional symport/antiport rules. We prove that problems beyond NP can also be solved. In particular, we show that deterministic systems of this type are able to solve all problems in the complexity class PP. Moreover, if non-deterministic systems are considered, then all problems in the class PSPACE can be solved.

Abstract In the framework of Membrane Computing, several efficient solutions to computationally hard problems have been given. To find new borderlines between families of P systems that can solve them and the ones that cannot is an important task to tackle the P versus NP problem. Adding syntactic and/or semantic ingredients can mean passing from non-efficiency to presumed efficiency. Here, we try to get narrow frontiers, setting the stage to adapt efficient solutions from a family of P systems to another one. In order to do that, a solution to the problem is given by means of a family of tissue P systems with evolutional symport/antiport rules and cell separation with the restriction that both the left-hand side and the right-hand side of the rules have at most two objects; that is, with recognizer P systems from $${\mathcal {TSEC}}(2, 2)$$ TSEC ( 2 , 2 ) . This result improves a previous one, when 3 objects could be used in the left-hand side of the evolutional communication rules

Spiking neural P systems with communication on request (SNQP systems) are neurally inspired computing devices, where a neuron actively seeks spikes from presynaptic neurons instead of passively waiting for spikes. In this work, we consider SNQP systems with mute rules (SNQPM systems), where mute rules have no communication functioning, namely the application of a mute rule only affects the number of spikes in the neuron where the rule resides, without effect on other neurons. It is demonstrated the computation capability of SNQPM systems with only mute rules does not exceed that of register machines with two registers, thereby not Turing universal. SNQPM systems are Turing universal when both mute rules and request rules are employed. Furthermore, two universal SNQPM systems with 7 neurons or 13 neurons are constructed as devices of number generating and function computing, respectively. Comparing to the universal SNQP system with 14 neurons and two types of spikes, SNQPM systems show the capability of trading-off mute rules and the types of spikes.

Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P systems with communication on request (abbreviated as SNQP systems) are a recently developed class of SNP system, where a neuron actively requests spikes from the neighboring neurons instead of passively receiving spikes. It is already known that small SNQP systems, with four unbounded neurons, can achieve Turing universality. In this context, 'unbounded' means that the number of spikes in a neuron is not capped. This work investigates the dependency of the number of unbounded neurons on the computation capability of SNQP systems. Specifically, we prove that (1) SNQP systems composed entirely of bounded neurons can characterize the family of finite sets of numbers; (2) SNQP systems containing two unbounded neurons are capable of generating the family of semilinear sets of numbers; (3) SNQP systems containing three unbounded neurons are capable of generating nonsemilinear sets of numbers. Moreover, it is obtained in a constructive way that SNQP systems with two unbounded neurons compute the operations of Boolean logic gates, i.e., OR, AND, NOT, and XOR gates. These theoretical findings demonstrate that the number of unbounded neurons is a key parameter that influences the computation capability of SNQP systems.

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements in efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this article, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insights into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness, and applicability to practical tasks, such as acoustic and visual pattern recognition. Our augmented approaches show several advanced learning properties and reliably outperform the baseline ones that use typical all-or-nothing spikes. Our approaches significantly improve the accuracies of a temporal-based approach on sound and MNIST recognition tasks to 99.38% and 97.90%, respectively, highlighting the effectiveness and potential merits of our methods. More importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them, including neuromorphic computing.

Large-scale multiobjective optimization problems (LSMOPs) are challenging for existing approaches due to the complexity of objective functions and the massive volume of decision space. Some large-scale multiobjective evolutionary algorithms (LSMOEAs) have recently been proposed, which have shown their effectiveness in solving some benchmarks and real-world applications. They merely focus on handling the massive volume of decision space and ignore the complexity of LSMOPs in terms of objective functions. The complexity issue is also important since the complexity grows along with the increment in the number of decision variables. Our previous study proposed a framework to accelerate evolutionary large-scale multiobjective optimization via problem reformulation for handling large-scale decision variables. Here, we investigate the effectiveness of LSMOF combined with decomposition-based MOEA (MOEA/D), aiming to handle the complexity of LSMOPs in both the decision and objective spaces. Specifically, MOEA/D is embedded in LSMOF via two different strategies, and the proposed algorithm is tested on various benchmark LSMOPs. Experimental results indicate the encouraging performance improvement benefited from the solution of the complexity issue in large-scale multiobjective optimization.

Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distribution of produced spikes are achieved by spiking rules. In this work, a novel mechanism for separately describing the integrate-and-fire behavior of neurons and the distribution of produced spikes, and a novel variant of the SNP systems, named evolution-communication SNP (ECSNP) systems, is proposed. More precisely, the integrate-and-fire behavior of neurons is achieved by spike-evolution rules, and the distribution of produced spikes is achieved by spike-communication rules. Then, the computational power of ECSNP systems is examined. It is demonstrated that ECSNP systems are Turing universal as number-generating devices. Furthermore, the computational power of ECSNP systems with a restricted form, i.e. the quantity of spikes in each neuron throughout a computation does not exceed some constant, is also investigated, and it is shown that such restricted ECSNP systems can only characterize the family of semilinear number sets. These results manifest that the capacity of neurons for information storage (i.e. the quantity of spikes) has a critical impact on the ECSNP systems to achieve a desired computational power.

Under certain smoothness assumptions, the Pareto set of a continuous multiobjective optimization problem is a piecewise continuous manifold in the decision space, which can be derived from the Karush-Kuhn-Tucker condition. Despite that a number of multiobjective evolutionary algorithms (MOEAs) have been proposed, their performance on multiobjective optimization problems with complicated Pareto sets (MOP-cPS) is still unsatisfying. In this article, we adopt the concept of manifold and propose a manifold learning-inspired mating strategy to enhance the diversity maintenance in MOEAs for solving MOP-cPS efficiently. In the proposed strategy, all of the individuals are first clustered into different manifolds according to their distribution in the objective space, and then the mating reproduction is restricted among individuals in the same manifold. Moreover, we embed the proposed mating strategy in three representative MOEAs and compare the embedded MOEAs with their original versions using the assortative genetic operators on a variety of MOP-cPS. The experimental results demonstrate the significant performance improvements benefitting from the proposed mating restriction strategy.

Spiking neural P (SN P) systems are a class of discrete neuron-inspired computation models, where information is encoded by the numbers of spikes in neurons and the timing of spikes. However, due to the discontinuous nature of the integrate-and-fire behavior of neurons and the symbolic representation of information, SN P systems are incompatible with the gradient descent-based training algorithms, such as the backpropagation algorithm, and lack the capability of processing the numerical representation of information. In this work, motivated by the numerical nature of numerical P (NP) systems in the area of membrane computing, a novel class of SN P systems is proposed, called numerical SN P (NSN P) systems. More precisely, information is encoded by the values of variables, and the integrate-and-fire way of neurons and the distribution of produced values are described by continuous production functions. The computation power of NSN P systems is investigated. We prove that NSN P is Turing universal as number generating devices, where the production functions in each neuron are linear functions, each involving at most one variable; as number accepting devices, NSN P systems are proved to be universal as well, even if each neuron contains only one production function. These results show that even if a single neuron is simple in the sense that it contains one or two production functions and the production functions in each neuron are linear functions with one variable, a network of simple neurons are still computationally powerful. With the powerful computation power and the characteristic of continuous production functions, developing learning algorithms for NSN P systems is potentially exploitable.

P systems are a class of parallel computational models inspired by the structure and functioning of living cells, where all the evolution rules used in a system are initially set up and keep unchanged during a computation. In this work, inspired by the fact that chemical reactions in a cell can be affected by both the contents of the cell and the environmental conditions, we introduce a variant of P systems, called P systems with rule production and removal (abbreviated as RPR P systems), where rules in a system are dynamically changed during a computation, that is, at any computation step new rules can be produced and some existing rules can be removed. The computational power of RPR P systems and catalytic RPR P systems is investigated. Specifically, it is proved that catalytic RPR P systems with one catalyst and one membrane are Turing universal; for purely catalytic RPR P systems, one membrane and two catalysts are enough for reaching Turing universality. Moreover, a uniform solution to the SAT problem is provided by using RPR P systems with membrane division. It is known that standard catalytic P systems with one catalyst and one membrane are not Turing universal. These results imply that rule production and removal is a powerful feature for the computational power of P systems.

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, different 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 efficiently 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.

A variety of evolutionary algorithms have been proposed for many-objective optimization in recent years. However, the difficulties in balancing the convergence and diversity of the population and selecting promising parents for offspring reproduction remain. In this paper, we propose a subregion division-based evolutionary algorithm with an effective mating selection strategy, termed SdEA, for many-objective optimization. In SdEA, a subregion division approach is proposed to divide the objective space into different subregions for balancing the diversity and convergence of the population. Besides, an effective mating selection strategy is proposed to enhance the diversity of the mating pool solutions, aimed at enhancing the selection probability of solutions in the sparse subregions. The proposed SdEA is compared with five state-of-the-art many-objective evolutionary algorithms on 23 test problems from DTLZ, WFG, and MaF test suites. Experimental results on these problems demonstrate that the proposed algorithm is competitive in solving many-objective problems. Furthermore, the proposed mating selection strategy is embedded in several evolutionary algorithms and experimental results demonstrate its effectiveness on improving the performance of the embedded algorithms.

This Fundamenta Informaticae special issue on "Bio-Inspired Computing: Theories and Applications" collects a selection of ten revised and extended papers presented at the 12th edition of the International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2017).BIC-TA is a series of conferences that aims to bring together researchers working in the main areas of natural computing inspired from biology, for presenting their recent results, exchanging ideas, and cooperating in a friendly framework.Since 2006, the conference was held in Wuhan

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.

Surrogate-assisted evolutionary algorithms (SAEAs) have been developed mainly for solving expensive optimization problems where only a small number of real fitness evaluations are allowed. Most existing SAEAs are designed for solving low-dimensional single or multiobjective optimization problems, which are not well suited for many-objective optimization. This paper proposes a surrogate-assisted many-objective evolutionary algorithm that uses an artificial neural network to predict the dominance relationship between candidate solutions and reference solutions instead of approximating the objective values separately. The uncertainty information in prediction is taken into account together with the dominance relationship to select promising solutions to be evaluated using the real objective functions. Our simulation results demonstrate that the proposed algorithm outperforms the state-of-the-art evolutionary algorithms on a set of many-objective optimization test problems.

Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike. It is proved that one type of spike is enough to guarantee the Turing universality of SNQ P systems. Theoretical results are shown in the cases of the SNQ P system used in both generating and accepting modes. Furthermore, the influence of the number of unbounded neurons (the number of spikes in a neuron is not bounded) on the computation power of SNQ P systems with one type of spike is investigated. It is found that SNQ P systems functioning as number generating devices with one type of spike and four unbounded neurons are Turing universal.

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.

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.

Tissue P systems are a class of bio-inspired computing models motivated by biochemical interactions between cells in a tissue-like arrangement. Tissue P systems with cell division offer a theoretical device to generate an exponentially growing structure in order to solve computationally hard problems efficiently with the assumption that there exists a global clock to mark the time for the system, the execution of each rule is completed in exactly one time unit. Actually, the execution time of different biochemical reactions in cells depends on many uncertain factors. In this work, with this biological inspiration, we remove the restriction on the execution time of each rule, and the computational efficiency of tissue P systems with cell division is investigated. Specifically, we solve subset sum problem by tissue P systems with cell division in a time-free manner in the sense that the correctness of the solution to the problem does not depend on the execution time of the involved rules.

Community detection is an important tool to analyze hidden information such as functional module and topology structure in complex networks. Compared with traditional community detection, it is more challenging to find overlapping communities in complex networks, especially when the networks are of large scales. Among various overlapping community detection techniques, the well-known clique percolation method (CPM) has shown promising performance in terms of quality of found communities, but suffers from serious curse of dimensionality due to its high computational complexity, which makes it very unlikely to be applied to large-scale networks. To address this issue, in this paper, we propose a weak-CPM for overlapping community detection in large-scale networks. A new measure for characterizing the similarity between weak cliques is also suggested to check whether the weak cliques can be merged into a community. Experimental results on synthetic and realworld networks demonstrate the competitive performance of the proposed method over six popular overlapping community detection algorithms in terms of both computational efficiency and quality of found communities. In addition, the proposed method is also suitable for detecting large-scale networks with an unclear community structure under different levels of overlapping density and overlapping diversity, which is an important property of many real-world complex networks.

Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in cells. One might expect the high level of co-appearance or repression of pathways to distinguish different subtypes of NSCLC. However, it is difficult to detect coordinated regulations of pathways by existing methods. In our work, the coordinated regulations of pathways are detected using modified higher logic analysis of gene expression data. Specifically, we identify the genes whose regulation obeys a logic function by the modified higher logic analysis, which focuses on the relationships among the gene triplets that are not evident when genes are examined in a pairwise fashion. Then, the relationships among genes are mapped to pathways to predict the coordinated regulated relationships among pathways. By comparing coordinated regulations of pathways, we find that the regulation patterns of pathways which are associated with cell death are different in three subtypes of NSCLC. This method allows us to uncover co-appearance or repression of pathways in high level, and it has a potential to distinguish the subtypes for complex diseases.

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

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).

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.

Numerical P systems are a class of P systems inspired both from the structure of living cells and from economics. In this work, we further investigate the generative capacity of numerical P systems as language generators. The families of languages generated by non-enzymatic, by enzymatic, and by purely enzymatic (all programs are enzymatic) numerical P systems working in the sequential mode are compared with the language families in the Chomsky hierarchy. Especially, a characterization of recursively enumerable languages is obtained by using purely enzymatic numerical P systems working in the sequential mode.

Spiking neural P systems (SN P systems for short) are a class of neural-like computing models in the framework of membrane computing. Inspired by the neurophysiological structure of the brain, SN P systems have been extended in various ways. P–Lingua

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.

This volume contains a selection of papers presented at the 9th edition of the international conference Bio-Inspired Computing: Theories and Applications, BIC

Cell signaling governs the basic cellular activities and coordinates the actions in cell. Abnormal regulations in cell signaling processing are responsible for many human diseases, such as diabetes and cancers. With the accumulation of massive data related to human cell signaling, it is feasible to obtain a human signaling network. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis to biological networks. In this work, we apply structural controllability to a human signaling network and detect driver nodes, providing a systematic analysis of the role of different proteins in controlling the human signaling network. We find that the proteins in the upstream of the signaling information flow and the low in-degree proteins play a crucial role in controlling the human signaling network. Interestingly, inputting different control signals on the regulators of the cancer-associated genes could cost less than controlling the cancer-associated genes directly in order to control the whole human signaling network in the sense that less drive nodes are needed. This research provides a fresh perspective for controlling the human cell signaling system.

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.

Spiking neural P systems (SN P systems, for short) with rules on synapses are a new variant of SN P systems, where the spiking and forgetting rules are placed on synapses instead of in neurons. Recent studies illustrated that this variant of SN P sys

Spiking neural P systems with weights are a new class of distributed and parallel computing models inspired by spiking neurons. In such models, a neuron fires when its potential equals a given value (called a threshold). In this work, spiking neural P systems with thresholds (SNPT systems) are introduced, where a neuron fires not only when its potential equals the threshold but also when its potential is higher than the threshold. Two types of SNPT systems are investigated. In the first one, we consider that the firing of a neuron consumes part of the potential (the amount of potential consumed depends on the rule to be applied). In the second one, once a neuron fires, its potential vanishes (i.e., it is reset to zero). The computation power of the two types of SNPT systems is investigated. We prove that the systems of the former type can compute all Turing computable sets of numbers and the systems of the latter type characterize the family of semilinear sets of numbers. The results show that the firing mechanism of neurons has a crucial influence on the computation power of the SNPT systems, which also answers an open problem formulated in Wang, Hoogeboom, Pan, Păun, and Pérez-Jiménez ( 2010 ).

Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by spiking neurons, where the spiking rules are usually used in a sequential way (an applicable rule is applied one time at a step) or an exhaustive way (an applicable rule is applied as many times as possible at a step). In this letter, we consider a generalized way of using spiking rules by "combining" the sequential way and the exhaustive way: if a rule is used at some step, then at that step, it can be applied any possible number of times, nondeterministically chosen. The computational power of SN P systems with a generalized use of rules is investigated. Specifically, we prove that SN P systems with a generalized use of rules consisting of one neuron can characterize finite sets of numbers. If the systems consist of two neurons, then the computational power of such systems can be greatly improved, but not beyond generating semilinear sets of numbers. SN P systems with a generalized use of rules consisting of three neurons are proved to generate at least a non-semilinear set of numbers. In the case of allowing enough neurons, SN P systems with a generalized use of rules are computationally complete. These results show that the number of neurons is crucial for SN P systems with a generalized use of rules to achieve a desired computational power.

Spiking neural P systems (SN P systems) are a class of distributed parallel computing devices inspired by the way neurons communicate by means of spikes; neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, with biological inspiration, we consider SN P systems with the restriction that at each step, one of the neurons (i.e., sequential mode) or all neurons (i.e., pseudo-sequential mode) with the maximum (or minimum) number of spikes among the neurons that are active (can spike) will fire. If an active neuron has more than one enabled rule, it nondeterministically chooses one of the enabled rules to be applied, and the chosen rule is applied in an exhaustive manner (a kind of local parallelism): the rule is used as many times as possible. This strategy makes the system sequential or pseudo-sequential from the global view of the whole network and locally parallel at the level of neurons. We obtain four types of SN P systems: maximum/minimum spike number induced sequential/pseudo-sequential SN P systems with exhaustive use of rules. We prove that SN P systems of these four types are all Turing universal as number-generating computation devices. These results illustrate that the restriction of sequentiality may have little effect on the computation power of SN P systems.

In a biological nervous system, astrocytes play an important role in the functioning and interaction of neurons, and astrocytes have excitatory and inhibitory influence on synapses. In this work, with this biological inspiration, a class of computation devices that consist of neurons and astrocytes is introduced, called spiking neural P systems with astrocytes (SNPA systems). The computation power of SNPA systems is investigated. It is proved that SNPA systems with simple neurons (all neurons have the same rule, one per neuron, of a very simple form) are Turing universal in both generative and accepting modes. If a bound is given on the number of spikes present in any neuron along a computation, then the computation power of SNPA systems is diminished. In this case, a characterization of semilinear sets of numbers is obtained.

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.

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.

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.

A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers, integers, and natural numbers. The power of the obtained systems is investigated. For instance, it is proved that integers (very restricted: 1, -1 for weights, 1 and 2 for firing thresholds, and as parameters in the rules) suffice for computing all Turing computable sets of numbers in both the generative and the accepting modes. When only natural numbers are used, a characterization of the family of semilinear sets of numbers is obtained. It is shown that spiking neural P systems with weights can efficiently solve computationally hard problems in a nondeterministic way. Some open problems and suggestions for further research are formulated.

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.

In this paper, a new genetic algorithm for solving multi-constrained optimization problems based on KS function is proposed. Firstly, utilizing the agglomeration features of KS function, all constraints of optimization problems are agglomerated to only one constraint. Then, we use genetic algorithm to solve the optimization problem after the compression of constraints. Finally, the simulation results on benchmark functions show the efficiency of our algorithm.

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.