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The pan-cancer initiative aims to study the origin patterns of cancer cell, the processes of carcinogenesis, and the signaling pathways from a perspective that spans across different types of cancer. The construction of the pan-cancer related gene regulatory network is helpful to excavate the commonalities in regulatory relationships among different types of cancers. It also aids in understanding the mechanisms behind cancer occurrence and development, which is of great scientific significance for cancer prevention and treatment. In light of the high dimension and large sample size of pan-cancer omics data, a causal pan-cancer gene regulation network inference algorithm based on stochastic complexity is proposed. With the network construction strategy of local first and then global, the stochastic complexity is used in the conditional independence test and causal direction inference for the candidate adjacent node set of the target nodes. This approach aims to decrease the time complexity and error rate of causal network learning. By applying this algorithm to the sample data of seven types of cancers in the TCGA database, including breast cancer, lung adenocarcinoma, and so on, the pan-cancer related causal regulatory networks are constructed, and their biological significance is verified. The experimental results show that this algorithm can eliminate the redundant regulatory relationships effectively and infer the pan-cancer regulatory network more accurately (https://github.com/LindeEugen/CNI-SC)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The white potato worm (Premnotrypes Vorax (Hustache)) is one of the pests that causes the greatest damage to the potato crop and the greatest economic losses to the grower; therefore, knowing its life cycle and estimating its intrinsic growth rate is crucial for selecting an appropriate chemical control method, in order to reduce the environmental impact and ensure a profitable production suitable for consumption. In this article, we present a fuzzy Malthusian model describing the evolution of the white potato worm in the crop, considering that the intrinsic growth rate and the reported initial data on the problem are of fuzzy nature. The main contributions and novelty of this paper are summarized in the following two aspects: first, the estimation of the intrinsic growth rate of the white potato worm, in function of the temperature, by using a Takagi-Sugeno-Kang type fuzzy rule-based model; and second, since in practice the initial white potato worm population in a crop is subjective, imprecise and vague, knowing the intrinsic growth rate, we propose and solve a fuzzy initial value problem to determine the evolution in time of the white potato worm population. In conclusion, given a weekly average temperature, it is possible to know the white potato worm population per unit area oscillating in an interval whose length depends on the degree of inaccuracy of the initial population and the intrinsic growth rate. This study can be relevant for grower decision making in terms of the type and frequency of pest control on his crop., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data. In the algorithm, we integrated multi-class sparse canonical correlation analysis (MSCCA) and molecular pathway analysis in order to derive discriminative molecular features that are correlated across different omics layers. We applied our proposed method to analyzing proteome and metabolome data of heart failure (HF), and extracted trans-omic biomarkers for HF subtypes; specifically, ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). We were able to detect not only individual proteins that were previously reported from single-omics studies but also correlated protein-metabolite pairs characteristic of HF disease subtypes. For example, we identified hexokinase1(HK1)-d-fructose-6-phosphate as a paired trans-omic biomarker for DCM, which could significantly perturb amino-sugar metabolism. Our proposed method is expected to be useful for various applications in precision medicine., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

In this paper, we examine how deep learning can be utilized to investigate neural health and the difficulties in interpreting neurological analyses within algorithmic models. The key contribution of this paper is the investigation of the impact of a dead neuron on the performance of artificial neural networks (ANNs). Therefore, we conduct several tests using different training algorithms and activation functions to identify the precise influence of the training process on neighboring neurons and the overall performance of the ANN in such cases. The aim is to assess the potential application of the findings in the biological domain, the expected results may have significant implications for the development of effective treatment strategies for neurological disorders. Successive training phases that incorporate visual and acoustic data derived from past social and familial experiences could be suggested to achieve this goal. Finally, we explore the conceptual analogy between the Adam optimizer and the learning process of the brain by delving into the specifics of both systems while acknowledging their fundamental differences., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The flux distribution in metabolic network can be decomposed as non-negative linear combinations of elementary flux modes (EFMs). Identifying biologically relevant EFM combination by decomposing flux distribution in metabolic network is a useful method to study metabolisms in systems biology. However, the occurrence of biologically irrelevant EFMs hinders the application of such methods. In this paper, we introduce a novel method for identifying EFM combination by minimizing enzyme mass. Our proposed method, called EMMD (Enzyme Mass Minimization Decomposition), takes into consideration both thermodynamic and enzymatic constraints in stoichiometry metabolic models. By implementing EMMD, we can decompose the flux distributions in metabolic network to detect biologically relevant EFM combinations. We demonstrate the effectiveness of our method by applying it to the core Escherichia coli metabolic network and show that the optimal EFM combinations identified by EMMD are unique. Moreover, the optimal EFM combination identified by EMMD not only aligns more closely with experimental values in terms of estimated growth rate, but it also demonstrates more favorable thermodynamics. Finally, we investigated the growth of the core Escherichia coli metabolic network in Luria-Bertani medium containing different carbon sources, revealing the impact of various carbon sources on the growth rate of flux distribution. EMMD thus could be a promising complement to the existing flux decomposition tools., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

In systems biology, models play a crucial role in understanding studied systems. There are many modelling approaches, among which rewriting systems provide a framework for describing systems on a mechanistic level. Describing biochemical processes often requires incorporating knowledge on an abstract level to simplify the system description or substitute the missing details. For this purpose, we present regulation mechanisms, an extension of this formalism with additional controls on the rewriting process. We introduce several regulation mechanisms and apply them to a rule-based language, a notation suitable for modelling biological phenomena. Finally, we demonstrate the usage of such regulations on several case studies from the biochemical domain., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Background and Objective: In the last two decades there can be observed a rapid development of systems biology. The basis of systems methods is a formal model of an analyzed system. It can be created in a language of some branch of mathematics and recently Petri net-based biological models seem to be especially promising since they have a great expressive power. One of the methods of analysis of such models is based on transition invariants. They correspond to some subprocesses which do not change a state of the modeled biological system. During such analysis, a need arose to study the subsets of transitions, what leads to interesting combinatorial problems - which have been considered in theory and practice., Methods & Results: Two problems of anti-occurrence were considered. These problems concern a set of transitions which is not a subset of any of t-invariant supports or is not a subset of t-invariant supports from some collection of such supports. They are defined in a formal way, their computational complexity is analyzed and an exact algorithm is provided for one of them., Conclusions: A comprehensive analysis of complex biological phenomena is challenging. Finding elementary processes that do not affect subprocesses belonging to the entire studied biological system may be necessary for a complete understanding of such a model and it is possible thanks to the proposed algorithm., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The analysis of evolutionary data allows uncovering information about the organisms and how they have adapted and evolved. This information could provide us with new insights about the specialisation of organisms (or part of them), how they adapt, how similar they are with other species, among others. Unfortunately, this evolutionary history can only be estimated, and for that, several computational methods exist. Among the methods, optimisation methods are one of the main approaches to deal with this problem, with multiobjective optimisation producing promising results. In this paper, we deal with multiobjective phylogenetic inference, using a multi-modal metaheuristic approach that exploits the decision space in the multiobjective formulation of the problem. In particular, we incorporate a new metric based on a topological tree distance. We compare the method with state of the art algorithms in terms of performance. Additionally, we perform a thorough analysis of a study case on a yeast Saccharomyces cerevisiae dataset. Results show that our proposal is able to improve the diversity of solutions while improving or keeping the quality of solutions in terms of hypervolume., (Copyright © 2022 Elsevier B.V. All rights reserved.)

In this paper, we consider reaction-diffusion systems, which describe the propagation of waves with chaotic and time periodic fronts. Using this property, we show that there exist reaction-diffusion models with a few of reagents, which, by a variation of initial data, is capable to generate all possible one-dimensional cell patterns. We describe algorithms, which allow to obtain any prescribed target cell patterns by chaotic waves. Our model can be considered as a reaction-diffusion analogue of universal Turing machine. So, we propose a new robust mechanism of positional information transfer, which, in contrast to Wolpert' gradients, can work at long distances. Universality of our model helps to explain why genes, responsible for morphogenesis, are highly conservative within long evolution periods., (Copyright © 2022 Elsevier B.V. All rights reserved.)

We present the novel approach to mathematical modeling of information processes in biosystems. It explores the mathematical formalism and methodology of quantum theory, especially quantum measurement theory. This approach is known as quantum-like and it should be distinguished from study of genuine quantum physical processes in biosystems (quantum biophysics, quantum cognition). It is based on quantum information representation of biosystem's state and modeling its dynamics in the framework of theory of open quantum systems. This paper starts with the non-physicist friendly presentation of quantum measurement theory, from the original von Neumann formulation to modern theory of quantum instruments. Then, latter is applied to model combinations of cognitive effects and gene regulation of glucose/lactose metabolism in Escherichia coli bacterium. The most general construction of quantum instruments is based on the scheme of indirect measurement, in that measurement apparatus plays the role of the environment for a biosystem. The biological essence of this scheme is illustrated by quantum formalization of Helmholtz sensation-perception theory. Then we move to open systems dynamics and consider quantum master equation, with concentrating on quantum Markov processes. In this framework, we model functioning of biological functions such as psychological functions and epigenetic mutation., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Protein-DNA interactions play a fundamental role in all life systems. A critical issue of such interactions is given by the strategy of protein search for specific targets on DNA. The mechanisms by which the protein are able to find relatively small cognate sequences, typically 15-20 base pairs (bps) for repressors, and 4-6 bps for restriction enzymes among the millions of bp of non-specific chromosomal DNA have hardly engaged researchers for decades. Recent experimental studies have generated new insights on the basic processes of protein-DNA interactions evidencing the underlying complex dynamic phenomena involved, which combine three-dimensional and one-dimensional motion along the DNA chain. It has been demonstrated that protein molecules have an extraordinary ability to find the target very quickly on the DNA chain, in some cases, with two orders of magnitude faster than the diffusion limit. This unique property of protein-DNA search mechanism is known as facilitated diffusion. Several theoretical mechanisms have been suggested to describe the origin of facilitated diffusion. However, none of such models currently has the ability to fully describe the protein search strategy. In this paper, we suggest that the ability of proteins to identify consensus sequences on DNA is based on the entanglement of π-π electrons between DNA nucleotides and protein amino acids. The π-π entanglement is based on Quantum Walk (QW), through Coin-position entanglement (CPE). First, the protein identifies a dimer belonging to the consensus sequence, and localize a π on such dimer, hence, the other π electron scans the DNA chain until the sequence is identified. Focusing on the example of recognition of consensus sequences of EcoRV or EcoRI, we will describe the quantum features of QW on protein-DNA complexes during the search strategy, such as walker quadratic spreading on a coherent superposition of different vertices and environment-supported long-time survival probability of the walker. We will employ both discrete- or continuous-time versions of QW. Biased and unbiased classical Random Walk (CRW) have been used for a long time to describe the Protein-DNA search strategy. QW, the quantum version of CRW, has been widely studied for its applications in quantum information applications. In our biological application, the walker (the protein) resides at a vertex in a graph (the DNA structural topology). Differently to CRW, where the walker moves randomly, the quantum walker can hop along the edges in the graph to reach other vertices entering coherently a superposition across different vertices spreading quadratically faster than CRW analogous evidencing the typical speed up features of the QW. When applied to a protein-DNA target search problem, QW gives the possibility to achieve the experimental diffusional motion of proteins over diffusion classical limits experienced along DNA chains exploiting quantum features such as CPE and long-time survival probability supported by the environment. In turn, we come to the conclusion that, under quantum picture, the protein search strategy does not distinguish between one-dimensional (1D) and three-dimensional (3D) cases., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The field of evolutionary algorithms (EAs) emerged in the area of computer science due to transfer of ideas from biology and developed independently for several decades, enriched with techniques from probability theory, complexity theory and optimization methods. In this paper, we consider some recent results form the EAs theory transferred back into biology. The well-known biotechnological procedure SELEX (Systematic Evolution of Ligands by EXponential enrichment) is viewed as an experimental implementation of an evolutionary algorithm. Theoretical bounds on EAs runtime are applied to model SELEX search for a regulatory region consisting of promoter and enhancer sequences. A comparison of theoretical bounds to the results of computational simulation indicates some cases where the theoretical bounds give favorable prediction, while simulation requires prohibitive computational resource., (Copyright © 2020. Published by Elsevier B.V.)

MicroRNAs (miRNAs) are widely involved in a series of significant biological processes, which have been revealed and verified by accumulating experimental studies. The computational inference of the correlation between miRNAs and diseases is essential to facilitate the detection of disease biomarkers for disease diagnosis, prevention, treatment and prognosis. In this paper, a model with Multiple use of Random Walk with restart algorithm was introduced for the prediction of the MiRNA-Disease Association (MRWMDA). Based on diverse similarity measures, the model first implemented the random walk with restart (RWR) algorithm on the integrated similarity network to construct the topological similarity of miRNAs and diseases, which took full advantage of the network topology information. Then, the RWR algorithm was applied in the miRNA topological similarity network, and a steady probability of each miRNA-disease pair was obtained to prioritize miRNA candidates. In particular, the initial probability of the RWR algorithm was determined by utilizing the combination of the recommendation algorithm and the maximum similarity method. The proposed model achieved significant improvement in prediction compared with previous models, with an AUC of 0.9353 and an AUPR of 0.4809. In addition, case studies of breast neoplasms and lung neoplasms representing different disease types further demonstrated the excellent ability of MRWMDA in detecting potential disease-associated miRNAs. These performance analyses indicated that MRWMDA could be an effective and powerful biological computational tool in relevant biomedical studies., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Cervical cancer cells may develop from any cell infected by human papillomavirus (HPV). The aim of this paper is to study whether an optimal control of HPV infection can reduce those resulting cancer cells. To this end, the problem will be modelled by five differential equations that describe the interactions between healthy cells, infected cells, free virus, precancerous cells and cancer cells. A saturated infection rate and two treatments are incorporated into the model. The first therapy stands for the efficacy of drug treatment in blocking new infections, whereas the second serves as the drug effectiveness in inhibiting viral production. First, The problem well-posedness is fulfilled in terms of existence, positivity and boundedness of solution. Next, the existence for the two optimal control pair is established, Pontryagin's maximum principle is used to characterize these two optimal controls. Moreover, the optimality system is derived and solved numerically using the forward and backward difference approximation scheme. Finally, numerical simulations are established in order to show the role of optimal therapy in controlling cancer cells proliferation. It was revealed that the antiviral drug therapies do not act only on the viral infection spread but also on reducing the amount of precancerous and cancerous cells. Consequently, the antiviral therapies can be considered amongst the most promising measures to reduce cervical cancer cells invasion., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Intercellular calcium signaling allows cells to communicate with each other and to interact with adjacent cells. Gap junction is the most common and important way for cellular communication. Recently, mathematical models have been widely used to gain a precise and quantitative understanding of the dynamics of intracellular calcium ions (Ca 2＋ ). In this paper, we establish a mathematical model considering the gap junction permeable to Ca 2＋ and to IP 3 for describing the calcium oscillations in coupled astrocytes. Store-operated calcium entry (SOCE) is viewed as the main process which controls the non-excitable cells, hence, we focus on the effect of store-operated calcium channel (SOCC) and receptor-operated calcium channel (ROCC) on the intercellular synchronization, respectively. By employing bifurcation analysis on this model, the dynamic behaviors of the coupled system with different physiological state cells is obtained with changes in the maximum capacity of the SOCC and the ROCC. The synchronization boundaries for different conditions are gained in the two parameters space of the channel parameters and the coupling strength. The results suggest that the variation of the maximum flow for different calcium channels determines the stable oscillations of the coupled system, as well as for the frequency and amplitude of oscillations. The SOCC has an expected effect on the change of the oscillatory interval while the ROCC demonstrated the influence on the amplitude modulation. Furthermore, the coupling strength and channel parameters could induce 1:1 locking of intercellular Ca 2＋ oscillations and the synchronization region like Arnol'd tongue is found., (Copyright © 2020. Published by Elsevier B.V.)

Global efforts are being made to achieve the clinical implementation of pre-emptive medicine for Staphylococcus aureus (S. aureus) infectious disease, which will realize both early detection at the pre-symptom stage and bacteriostatic therapy by antibiotic-free medicine in a future. Several research groups proposed the intercellular signal transduction factor (auto-inducing peptide: AIP) antibody, the synthesised AIP analogues and a cyclic depsipeptide with high constitutional similarity to AIP as a candidate of the pre-emptive medicine for S. aureus infectious disease. In this paper, to evaluate a validity of them, we mathematically explored both a pre-symptom associated with the pathogenic expression process of S. aureus and several therapeutic targets that delay or suppress the appearance of the pre-symptom. The stochastic mathematical analysis identified a peak of fluctuation in intracellular AgrD concentration as the pre-symptom. Moreover, employing parameter sensitivity analysis, the enhancement of binding inhibition between AgrC receptor and AIP was identified as effective therapeutic target. Based on these findings, we evaluated a feasibility of above-mentioned candidates, and concluded that the continuous application of AgrC receptor antagonists, such as the synthesised AIP analogues and a cyclic depsipeptide with high constitutional similarity to AIP, is useful as pre-emptive medicine for S. aureus infectious disease., (Copyright © 2020. Published by Elsevier B.V.)

M systems are mathematical models of morphogenesis developed to gain insights into its relations to phenomena such as self-assembly, self-controlled growth, homeostasis, self-healing and self-reproduction, in both natural and artificial systems. M systems rely on basic principles of membrane computing and self-assembly, as well as explicit emphasis on geometrical structures (location and shape) in 2D, 3D or higher dimensional Euclidean spaces. They can be used for principled studies of these phenomena, both theoretically and experimentally, at a computational level abstracted from their detailed implementation. In particular, they afford 2D and 3D models to explore biological morphogenetic processes. Theoretical studies have shown that M systems are powerful tools (e.g., computational universal, i.e. can become as complex as any computer program) and their parallelism allows for trading space for time in solving efficiently problems considered infeasible on conventional computers (NP-hard problems). In addition, they can also exhibit properties such as robustness to injuries and degrees of self-healing. This paper focuses on the experimental side of M systems. To this end, we have developed a high-level morphogenetic simulator, Cytos, to implement and visualize M systems in silico in order to verify theoretical results and facilitate research in M systems. We summarize the software package and make a brief comparison with some other simulators of membrane systems. The core of the article is a description of a range of experiments inspired by aspects of morphogenesis in both prokaryotic and eukaryotic cells. The experiments explore the regulatory role of the septum and of the cytoskeleton in cell fission, the robustness of cell models against injuries, and, finally, the impact of changing nutrient concentration on population growth., (Copyright © 2020. Published by Elsevier B.V.)

This paper proposes a methodological approach to analyzing the evolution of the stability of socioeconomic systems and to assessing the risk of their possible destabilization based on the use of mathematical modeling methods. In this paper, a basic model is presented allowing us to describe the joint dynamics of processes in the economic, organizational, and sociopsychological areas of society. The model shows at what parameters of the socioeconomic system its steady functioning is possible, and at which it is impossible. It is shown that the transition from its steady state to an unsteady one is not smooth but occurs as a leap. This methodology is applied to the analysis of stability and change in the Egyptian socioeconomic system after 2010., (Copyright © 2020 Elsevier B.V. All rights reserved.)

An economic system is thought to be evolutionary growing. Economists investigate human decision-making by monetary value. However, the emergent property of the economy has not been explained. A model considering the extent of the monetary value is required. In this paper, a time-space model is proposed to elucidate the economic evolution of a market. Since the market activities are always open and realtime, the model of time and space is complicated. For the complexity, the probabilistic boundary and expectations under a low-possibility are introduced. Then, time and space cannot be separated and are defined as an asynchronous relationship. We apply this time-space model to the adaptive agent models of single and double auctions. The results of the numerical simulation indicate the intermittency in a broad area of the control parameter. This result concludes that the extent of monetary value can be a basis of the evolutionary market., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In this paper, we identify all possible Gray Code and Partitioned Gray Code representations of the Universal Genetic Code for n = 2-bit and 3-bit binary numbers. We analyse the Hamming Distance matrices of all these Gray code and Partitioned Gray Code possibilities for which we obtain the Toeplitz and Partitioned Toeplitz Matrices, respectively. We use this Gray Code and Partitioned Gray Code representations of the Universal Genetic Code combined with the novel Toeplitz matrix approach to generate many Never Born Protein (NBP) Sequences, which exhibit intrinsic structural stability. In general, Never Born Protein sequences may have many potential applications in synthetic biology and opens a new vista in understanding this new subset of proteins for better applications in drug discovery, synthesis of fine chemicals, etc., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The goal of the segmentation of brain images is to separate the images in different non-compatible homogenous areas reflecting the numerous anatomical structures. Brain segmentation by magnetic resonance has numerous implications for diagnosing brain disorganizations such as Alzheimer's, Parkinson-related syndrome among others. However, it is not an simple job to automatically segment the MR image. The main motive of this study is to provide a better segmentation approach for the segment of the ROI (Region Of Interest) region from the MRI image by solving the issues that currently exist in the literary works. MRI segmentation is not a trivial task, because acquired MR images are imperfect and are often corrupted by noise and other image artifacts. The variety in technologies for image processing has contributed to the creation in numerous image segmentation techniques. That is because there is no universal approach, nor are all methods necessarily appropriate for a specific form of picture suitable for all pictures. Other approaches still use the gray level histogram, for example, while others integrate detailed spatial picture details for bleeding conditions. Some methods use statistical techniques, but some do incorporate existing information to enhance segmentation efficiency. Some methods utilize probabilistic or fuzzy methods. Yet there are certain inconveniences of all the current processes. Therefore, we have intended to propose a new segmentation approach for the ROI region segmentation. The proposed work comprised of three phases namely preprocessing, edge detection and segmentation. At first, the MRI images are extracted from the database and that each of the input images is enhanced by applying a high pass filter. After completing the preprocessing method, the enhanced canny edge detection (ECED) approach is used to enhance the image. After that, the images are given to the modified watershed segmentation (MWS) algorithm which separates the ROI part from MRI Image. The testing consequences demonstrate that the proposed system accomplishes to give the good result related to the available strategies. Xilinx Virtex-5 FPGA is used to implement in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Disease Gene Association finds genes that are involved in the presentation of a given genetic disease. We present a hybrid approach which implements a multi-objective genetic algorithm, where input consists of centrality measures based on various relational biological evidence types merged into a complex network. Multiple objective settings and parameters are studied including the development of a new exchange methodology, safe dealer-based crossover. Successful results with respect to breast cancer and Parkinson's disease compared to previous techniques and popular known databases are shown. In addition, the newly developed methodology is also successfully applied to Alzheimer's disease, further demonstrating its flexibility. Across all three case studies the strongest results were produced by the shortest path-based measures stress and betweenness, either in a single objective parameter setting or when used in conjunction in a multi-objective environment. The new crossover technique achieved the best results when applied to Alzheimer's disease., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Biological systems are difficult to understand complex systems. Scientists continue to create models to simulate biological systems but these models are complex too; for this reason, new reduction methods to simplify complex biological models into simpler ones are increasingly needed. In this paper, we present a way of reducing complex quantitative (continuous) models into logical models based on time windows of system activity and logical (Boolean) models. Time windows were used to define slow and fast activity areas. We use the proposed approach to reduce a continuous ODE model into a logical model describing the G1/S checkpoint with and without DNA damage as a case study. We show that the temporal unfolding of this signalling system can be broken down into three time windows where only two display high level of activity and the other shows little or no activity. The two active windows represent a cell committing to cell cycle and making the G1/S transition, respectively, the two most important high level functions of cell cycle in the G1 phase. Therefore, we developed two models to represent these time windows to reduce time complexity and used Boolean approach to reduce interaction complexity in the ODE model in the respective time windows. The developed reduced models correctly produced the commitment to cell cycle and G1/S transfer through the expected behavior of signalling molecules involved in these processes. As most biological models have a large number of fast reactions and a relatively smaller number of slow reactions, we believe that the proposed approach could be suitable for representing many, if not all biological signalling networks. The approach presented in this study greatly helps in simplifying complex continuous models (ODE models) into simpler models. Moreover, it will also assist scientists build models concentrating on understanding and representing system behavior rather than setting values for a large number of kinetic parameters., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In epidemic control, how the size of treatment blocks influences the disease is a very interesting problem. Here, we construct an SIS model with spatially clustered treatment blocks using cellular automata and pair approximation to investigate the disease control and optimal arrangement of treatment blocks. On a same treatment intensity, the treatment blocks are built randomly and the individuals in the treatment blocks are cured in the next interval. The migration and neighboring structures are also considered in this model. We find that the size of treatment blocks will influence the control of disease. The bigger size of treatment blocks is more effective in epidemic control. The results obtained by lattice simulations and pair approximation respectively are consistent. On the same treatment arrange, higher migration rate has completely different influence on the epidemic depending on different neighboring structures. Besides, in different neighboring structures, both infected and susceptible populations will get clustered under the treatment blocks. Meanwhile, the neighboring structures can influence the degree of aggregation and the bigger treatment blocks can promote the aggregation compared to the smaller ones. The results here are significant for the control of epidemic diseases, especially when available medical resources are limited., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Considering biosphere as a closed thermodynamical system, inside it, biological organisms may be considered as open thermodynamical systems exchanging energy and matter with ecosystem. Making appeal to non-equilibrium thermodynamics, anthropology, biology, sociology and cliodynamics, we discuss, from an interdisciplinary stance, some main moments of human evolution from either the phylogenetic and the ontogenetic standpoint, highlighting the crucial bifurcation which saw the rising of the modern societies with respect to primitive ones., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In this paper, we investigate a new variant of spiking neural P systems (SNP systems, in short), called spiking neural P systems with multiple channels and polarizations (SNP-MCP systems, in short). The variant integrates two interesting features: multiple channel and polarization. Each neuron can use its multiple channels to connect one or more subsequent sets of neurons. Moreover, both polarizations and regular expressions are used in rules to control the spiking of neurons. The computational power of the variant is discussed. The Turing universality of the variant as number generating/accepting devices is proven, and then a small universal system with 150 neurons is constructed to compute any Turing computable function., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In this paper, we explore the occurrence of the hydra effect in food chains, a popular research theme in the current decade. The hydra effect, one of the paradoxical results in theoretical and applied ecology refers to the fact where increasing mortality rate on a population enhances its own stock. The main focus is to propose a dynamical system model of food chain showing a stable steady state and estimate the variation of stock of targeted species with increasing mortality. In our model, the per capita growth rate of any predator trophic level does not depend upon its density. The prey-predator model incorporating such a feature for predator growth is referred to as 'pure predator system' (see Sieber and Hilker (2012), J. Math. Biol. (2012) 64: 341-360, Journal of Mathematical Biology). Keeping the above feature in mind, we study a Rosenweig-MacArthur food chain model with logistic prey growth and Holling type II functional responses. It is shown that hydra effect at stable state appears on (a) prey in a four-trophic system, (b) first predator in a five-trophic system, and (c) prey and second predator in a six-trophic system. Xiao and Cao (2009) (Mathematical and Computer Modelling 50 (2009) 360-379) established that limit cycle may be observed due to harvesting in a system with the ratio-dependent prey-predator system (example of a non "non-pure predator system"). Therefore, if harvesting causes instability on some range of mortality rate, the hydra effect cannot occur at a stable state. Some results show that the unique stable steady state in our model remains stable under harvesting of either trophic level. As a whole, our investigations have some contribution in understanding population interactions, fishery management and biological pest control tactic., (Copyright © 2019 Elsevier B.V. All rights reserved.)

DNA computing, as one of potential means to solve complicated computational problems, is a new field of interdisciplinary research, including computational mathematics, parallel algorithms, bioinformatics. Capacitated vehicle routing problem is one of famous NP-hard problems, which includes determining the path of a group same vehicles serving a set of clients, while minimizing the total transportation cost. Based on the bio-heuristic computing model and DNA molecular manipulations, parallel biocomputing algorithms for solving capacitated vehicle routing problem are proposed in this paper. We appropriately use different biological chains to mean vertices, edges, weights, and adopt appropriate biological operations to search the solutions of the problem with O(n 2 ) time complexity. We enrich the application scope of biocomputing, reduce computational complexity, and verify practicability of DNA parallel algorithms through simulations., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In this paper we studied a resonate and fire relaxation oscillator subject to time dependent modulation to investigate phase-locking phenomena occurring in neurophysiological systems. The neural model (denoted LFHN) was obtained by linearization of the FitzHugh-Nagumo neural model near an hyperbolic fixed point and then by introducing an integrate-and-fire mechanism for spike generation. By employing specific tools to study circle maps, we showed that this system exhibits several phase-locking patterns in the presence of periodic perturbations. Moreover, both the amplitude and frequency of the modulation strongly impact its phase-locking properties. In addition, general conditions for the generation of firing activity were also obtained. In addition, it was shown that for moderate noise levels the phase-locking patterns of the LFHN persist. Moreover, in the presence of noise, the rotation number changes smoothly as the stimulation current increases. Then, the statistical properties of the firing map were investigated too. Lastly, the results obtained with the forced LFHN suggest that such neural model could be used to fit specific experimental data on the firing times of neurons., (Copyright © 2019 Elsevier B.V. All rights reserved.)

A gene regulatory network (GRN) represents a set of genes along with their regulatory interactions. Cellular behavior is driven by genetic level interactions. Dynamics of such systems show nonlinear saturation kinetics which can be best modeled by Michaelis-Menten (MM) and Hill equations. Although MM equation is being widely used for modeling biochemical processes, it has been applied rarely for reverse engineering GRNs. In this paper, we develop a complete framework for a novel model for GRN inference using MM kinetics. A set of coupled equations is first proposed for modeling GRNs. In the coupled model, Michaelis-Menten constant associated with regulation by a gene is made invariant irrespective of the gene being regulated. The parameter estimation of the proposed model is carried out using an evolutionary optimization method, namely, trigonometric differential evolution (TDE). Subsequently, the model is further improved and the regulations of different genes by a given gene are made distinct by allowing varying values of Michaelis-Menten constants for each regulation. Apart from making the model more relevant biologically, the improvement results in a decoupled GRN model with fast estimation of model parameters. Further, to enhance exploitation of the search, we propose a local search algorithm based on hill climbing heuristics. A novel mutation operation is also proposed to avoid population stagnation and premature convergence. Real life benchmark data sets generated in vivo are used for validating the proposed model. Further, we also analyze realistic in silico datasets generated using GeneNetweaver. The comparison of the performance of proposed model with other existing methods shows the potential of the proposed model., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Cilia and flagella are cell organelles serving basic roles in cellular motility. Ciliary movement is performed by a sweeping-like repeated bending motion, which gives rise to a self-propagating "ciliary beat". The hallmark structure in cilia is the axoneme, a stable architecture of microtubule doublets. The motion of axoneme is powered by the axonemal dynein motor family powered by ATP hydrolysis. It is still unclear how the organized beat of cilium and flagella emerges from the combined action of hundreds of dynein molecules. It has been hypothesized that such coordination is mediated by mechanical stress due to transverse, radial or sliding deformations. The beating asymmetry is crucial for airway ciliary function and it requires tubulin glutamination a unique posttranslational modification of C-termini of constituent microtubules that is highly abundant in cilia and flagella. The exact role of tubulin glutamination in ciliary or flagellar function is still unclear. In this paper we analyze the role of calcium (Ca 2+ ) ions based on the experimental evidence that the flagellar asymmetry can be increased due to the entry of extracellular Ca 2+ through, for example, the nimodipine-sensitive pathway located in the flagella. We propose a new scenario based on the polyelectrolyte properties of cellular microtubules (MTs) such that dynamic influx of Ca 2+ ions provides the initiation and synchronization of dynein sliding along microtubules. We also point out the possible interplay between tubulin polyglutaminated C-termini and localized pulses of Ca 2+ ions along microtubules., (Copyright © 2019 Elsevier B.V. All rights reserved.)

As a type of model-based metaheuristic, estimation of distribution algorithms (EDAs) show certain advantages over other metaheuristics by using statistical learning method to estimate the distribution of promising solutions. However, the commonly-used Gaussian EDAs (GEDAs) usually suffer from premature convergence that severely limits their efficiency. In this paper, we first attempt to enhance the performance of GEDA by improving its model estimation method. The new estimation method shifts the weighted mean of high-quality solutions towards the fitness improvement direction and estimates the covariance matrix by taking the shifted mean as the center. Theoretical analyses show that the new covariance matrix is essentially a rank-one modification (R1M) of the original one. It could effectively adjust both the search scope and the search direction of GEDA, and thus improving the search efficiency. Furthermore, considering the importance of the population size tuning in GEDA, we develop a population reduction (PR) strategy which linearly reduces the population size throughout the evolution. By this means, the exploration and exploitation ability of GEDA could be balanced better in different search stages and a more proper utilization of limited computation resource can be achieved. Combining GEDA with the R1M and PR strategies, a novel EDA variant named EDA-R1M-PR is developed. The performance of EDA-R1M-PR was comprehensively evaluated and compared with that of several state-of-the-art evolutionary algorithms. Experimental results indicate that the R1M and PR strategies effectively enhance the global optimization ability of GEDA and the resultant EDA-R1M-PR significantly outperforms its competitors on a set of benchmark functions., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In structured populations the spatial arrangement of cooperators and defectors on the interaction graph together with the structure of the graph itself determines the game dynamics and particularly whether or not fixation of cooperation (or defection) is favored. For networks described by regular graphs and for a single cooperator (and a single defector) the question of fixation can be addressed by a single parameter, the structure coefficient. This quantity is invariant with respect to the location of the cooperator on the graph and also does not vary over different networks. We may therefore consider it to be generic for regular graphs and call it the generic structure coefficient. For two and more cooperators (or several defectors) fixation properties can also be assigned by structure coefficients. These structure coefficients, however, depend on the arrangement of cooperators and defectors which we may interpret as a configuration of the game. Moreover, the coefficients are specific for a given interaction network modeled as a regular graph, which is why we may call them specific structure coefficients. In this paper, we study how specific structure coefficients vary over interaction graphs and analyze how spectral properties of interaction networks relate to specific structure coefficients. We also discuss implications for the benefit-to-cost ratios of donation games., (Copyright © 2019 Elsevier B.V. All rights reserved.)

One of the possible ways to visualize the effect of intra- and inter-species synergistic and antagonistic interactions in a natural ecosystem is the detailed analysis of the underlying prey-predator model, and the subsequent analytical findings may provide a definite justification towards the species coexistence, which often maintains biodiversity in nature. Here, our central motivation is to understand the combined effect of the Allee threshold and intra-species competition on the evolution of interacting species, which often experience delays in evolution due to its complex ecological and physiological processes. Therefore, in the present paper, we extensively analyze the stability of a two-delayed prey-predator system in the presence of strong Allee effects in prey and intra-species competition in predator. In addition, we capture the reality of time difference between the lifespan of the prey and predator through proper nondimensionalization. The two delays in the proposed retarded system correspond to the intra-specific competition-induced feedback time lag to the prey and predator gestation period. In the absence of intra-predator competition, the present dynamics unveils supercritical Hopf-bifurcation around the interior point of coexistence which is in-line with the existing literature. It is noteworthy to mention that the proposed Allee system exhibits subcritical Hopf-bifurcation in the presence of intra-species competition in predator. We confirm the occurrence of both supercritical and subcritical Hopf-bifurcations via calculating the direction and stability of Hopf-bifurcating periodic solutions using the normal form method and the center manifold theory. Moreover, the suggested delayed schema presents supercritical Hopf-bifurcation at the boundary steady-state, where the population density of prey exists at its maximum carrying capacity. We recognize the bistability between extinction and coexistence, and the proposed model also exhibits the 'chaotic concurrence between prey and predator,' which happens through the period-doubling bifurcation. The existence of chaos is validated using the estimated power spectrum and the spectrum of Lyapunov exponents. The primary finding of this paper is that Allee threshold induces the capability to the density-dependent death rate of predator towards changing the stability of 'oscillatory coexistence between prey and predator.', (Copyright © 2019 Elsevier B.V. All rights reserved.)

As reinforcement learning algorithms are being applied to increasingly complicated and realistic tasks, it is becoming increasingly difficult to solve such problems within a practical time frame. Hence, we focus on a satisficing strategy that looks for an action whose value is above the aspiration level (analogous to the break-even point), rather than the optimal action. In this paper, we introduce a simple mathematical model called risk-sensitive satisficing (RS) that implements a satisficing strategy by integrating risk-averse and risk-prone attitudes under the greedy policy. We apply the proposed model to the K-armed bandit problems, which constitute the most basic class of reinforcement learning tasks, and prove two propositions. The first is that RS is guaranteed to find an action whose value is above the aspiration level. The second is that the regret (expected loss) of RS is upper bounded by a finite value, given that the aspiration level is set to an "optimal level" so that satisficing implies optimizing. We confirm the results through numerical simulations and compare the performance of RS with that of other representative algorithms for the K-armed bandit problems., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

The revelation of compositional principles of the organization of long DNA sequences is one of the crucial tasks in the study of biosystems. This paper is devoted to the analysis of compositional differences between real DNA sequences and Markov-like randomly generated similar sequences. We formulate, among other things, a generalization of Chargaff's second rule and verify it empirically on DNA sequences of five model organisms taken from Genbank. Moreover, we apply the same frequency analysis to simulated sequences. When comparing the afore mentioned - real and random - sequences, significant similarities, on the one hand, as well as essential differences between them, on the other hand, are revealed and described. The significance and possible origin of these differences, including those from the viewpoint of maximum informativeness of genetic texts, is discussed. Besides, the paper discusses the question of what is a "long" DNA sequence and quantifies the choice of length. More precisely, the standard deviations of relative frequencies of bases stabilize from the length of approximately 100 000 bases, whereas the deviations are about three times as large at the length of approximately 25 000 bases., (Copyright © 2019 Elsevier B.V. All rights reserved.)

The fuzzy cognitive map (FCM) is an effective tool for modeling dynamic decision support systems. It describes the analyzed phenomenon in the form of key concepts and the causal connections between them. The main aspects of the building of the FCM model are: concepts selection, determining the output concepts, criterion selection, and determining the relationships between concepts. It is usually based on expert knowledge. The main goal of the paper is to define the optimal in some sense FCM structure through the introduction of the notion of output concepts and minimizing the number of concepts and connections between them. The proposed approach allows for: (1) the selection of key concepts based on graph theory metrics and determining the connections between them; (2) the determination of the criterion of learning based on output concepts and fitting the learning process to the analyzed problem. A simulation analysis was done with the use of synthetic and real-life data. Experiments confirm that the proposed approach improves the learning process compared to the standard approaches., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Gene expression microarray classification is a crucial research field as it has been employed in cancer prediction and diagnosis systems. Gene expression data are composed of dozens of samples characterized by thousands of genes. Hence, an accurate and effective classification of such samples is a challenge. Machine learning techniques have been broadly utilized to build substantial and precise classification models. This paper proposes a new classification technique for gene expression data, which is called Modified k-nearest neighbor (MKNN). MKNN is applied in two scenarios namely; smallest modified KNN (SMKNN) and largest modified KNN (LMKNN). Both implementations are undertaken to enhance the performance of KNN. The key idea is to employ robust neighbors from training data by using a new weighting strategy. Several experiments have been performed on six different gene expression datasets. Experiments have shown that MKNN in its both scenarios outperforms traditional as well as recent ones. MKNN has been compared against (i) KNN, (ii) weighted KNN, (iii) support vector machine (SVM), (iv) fuzzy support vector machine, (v) brain emotional learning (BEL) in terms of classification accuracy, precision, and recall. On the other hand, results show that MKNN introduces smaller testing time than both KNN and weighted KNN., (Copyright © 2019 Elsevier B.V. All rights reserved.)

A set X of 20 trinucleotides has been found to have the highest average occurrence in the reading frame, compared to the two shifted frames, of genes of bacteria, archaea, eukaryotes, plasmids and viruses (Michel, 2015, 2017; Arquès and Michel, 1996). This set X has an interesting mathematical property, since X is a maximal C 3 self-complementary trinucleotide circular code (Arquès and Michel, 1996). Furthermore, any motif obtained from this circular code X has the capacity to retrieve, maintain and synchronize the reading frame in genes. In a recent study of the X motifs in the complete genome of the yeast, Saccharomyces cerevisiae, it was shown that they are significantly enriched in the reading frame of the genes (protein-coding regions) of the genome (Michel et al., 2017). It was suggested that these X motifs may be evolutionary relics of a primitive code originally used for gene translation. The aim of this paper is to address two questions: are X motifs conserved during evolution? and do they continue to play a functional role in the processes of genome decoding and protein production? In a large scale analysis involving complete genomes from four mammals and nine different yeast species, we highlight specific evolutionary pressures on the X motifs in the genes of all the genomes, and identify important new properties of X motif conservation at the level of the encoded amino acids. We then compare the occurrence of X motifs with existing experimental data concerning protein expression and protein production, and report a significant correlation between the number of X motifs in a gene and increased protein abundance. In a general way, this work suggests that motifs from circular codes, i.e. motifs having the property of reading frame retrieval, may represent functional elements located within the coding regions of extant genomes., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Several species of fish live in groups to increase their foraging efficiency and reproduction rates. Such groups are considered self-organized since they can adopt different cooperative actions without the presence of an apparent leader. One of their most interesting collaborative behaviors found in fish is the hunting strategy presented by the Yellow Saddle Goatfish (Parupeneus cyclostomus). In this strategy, the complete group of fish is distributed in subpopulations to cover the whole hunting region. In each sub-population, all fish participate collectively in the hunt considering two different roles: chaser and blocker. In the hunt, a chaser fish actively tries to find the prey in a certain area whereas a blocker fish moves spatially to avoid the escape of the prey. In this paper, we develop the hunting model of Yellow Saddle Goatfish, which at some abstraction level can be characterized as a search strategy for optimization proposes. In the approach, different computational operators are designed in order to emulate this peculiar hunting behavior. With the use of this biological model, the new search strategy improves the optimization results in terms of accuracy and convergence in comparison to other popular optimization techniques. The performance of this method is tested by analyzing its results with other related evolutionary computation techniques. Several standard benchmark functions commonly used in the literature were considered to obtain optimization results. Furthermore, the proposed model is applied to solve certain engineering optimization problems. Analysis of the experimental results exhibits the efficiency, accuracy, and robustness of the proposed algorithm., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Automated methods for reconstructing biological networks are becoming increasingly important in computational systems biology. Public databases containing information on biological processes for hundreds of organisms are assisting in the inference of such networks. This paper proposes a multiobjective genetic algorithm method to reconstruct networks related to metabolism and protein interaction. Such a method utilizes structural properties of scale-free networks and known biological information about individual genes and proteins to reconstruct metabolic networks represented as enzyme graph and protein interaction networks. We test our method on four commonly-used protein networks in yeast. Two are networks related to the metabolism of the yeast: KEGG and BioCyc. The other two datasets are networks from protein-protein interaction: Krogan and BioGrid. Experimental results show that the proposed method is capable of reconstructing biological networks by combining different omics data and structural characteristics of scale-free networks. However, the proposed method to reconstruct the network is time-consuming because several evaluations must be performed. We parallelized this method on GPU to overcome this limitation by parallelizing the objective functions of the presented method. The parallel method shows a significant reduction in the execution time over the GPU card which yields a 492-fold speedup., (Copyright © 2018 Elsevier B.V. All rights reserved.)

The paper aims to investigate the dynamical behavior of a predator-prey system with Holling type IV functional response in which both the species are subject to capturing. We mainly consider how the harvesting affects equilibria, stability, limit cycles and bifurcations in this system. We adopt the method of qualitative and quantitative analysis, which is based on the dynamical theory, bifurcation theory and numerical simulation. The boundedness of solutions, the existence and stability of equilibrium points of the system are further studied. Based on the Sotomayor's theorem, the existence of transcritical bifurcation and saddle-node bifurcation are derived. We use the normal form theorem to analyze the Hopf bifurcation. Simulation results show that the first Lyapunov coefficient is negative and a stable limit cycle may bifurcate. Numerical simulations are performed to make analytical studies more complete. This work illustrates that using the harvesting effort as control parameter can change the behaviors of the system, which may be useful for the biological management., (Copyright © 2018 Elsevier B.V. All rights reserved.)

An elementary flux mode (EFM) is a pathway with minimum set of reactions that are functional in steady-state constrained space. Due to the high computational complexity of calculating EFMs, different approaches have been proposed to find these flux-balanced pathways. In this paper, an approach to find a subset of EFMs is proposed based on a graph data model. The given metabolic network is mapped to the graph model and decisions for reaction inclusion can be made based on metabolites and their associated reactions. This notion makes the approach more convenient to categorize the output pathways. Implications of the proposed method on metabolic networks are discussed., (Copyright © 2017 Elsevier B.V. All rights reserved.)

In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Mathematical modelling is fundamental to understand the dynamic behavior and regulation of the biochemical metabolisms and pathways that are found in biological systems. Pathways are used to describe complex processes that involve many parameters. It is important to have an accurate and complete set of parameters that describe the characteristics of a given model. However, measuring these parameters is typically difficult and even impossible in some cases. Furthermore, the experimental data are often incomplete and also suffer from experimental noise. These shortcomings make it challenging to identify the best-fit parameters that can represent the actual biological processes involved in biological systems. Computational approaches are required to estimate these parameters. The estimation is converted into multimodal optimization problems that require a global optimization algorithm that can avoid local solutions. These local solutions can lead to a bad fit when calibrating with a model. Although the model itself can potentially match a set of experimental data, a high-performance estimation algorithm is required to improve the quality of the solutions. This paper describes an improved hybrid of particle swarm optimization and the gravitational search algorithm (IPSOGSA) to improve the efficiency of a global optimum (the best set of kinetic parameter values) search. The findings suggest that the proposed algorithm is capable of narrowing down the search space by exploiting the feasible solution areas. Hence, the proposed algorithm is able to achieve a near-optimal set of parameters at a fast convergence speed. The proposed algorithm was tested and evaluated based on two aspartate pathways that were obtained from the BioModels Database. The results show that the proposed algorithm outperformed other standard optimization algorithms in terms of accuracy and near-optimal kinetic parameter estimation. Nevertheless, the proposed algorithm is only expected to work well in small scale systems. In addition, the results of this study can be used to estimate kinetic parameter values in the stage of model selection for different experimental conditions., (Copyright © 2017 Elsevier B.V. All rights reserved.)

The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation, man-machine interface and creative design. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Graphs can be used as contact networks in models of epidemic spread. Most research seeks to extract the properties of an extant graph, derived from questionnaires or other sources of contact information. The inverse problem of searching the space of graphs for those that exhibit specific properties has received little attention and that is the focus of this study. This is, in part, because searching the space of contact networks is difficult. This paper extends and tests a representation for searching the space of contact networks with evolutionary computation. The focus of this study is on improvements in the representation used to evolve potential contact networks, adding an operator that permits strictly local adjustments to connectivity of the network, and another that does nothing at all. The benefits of doing nothing at some points during the construction of a network are substantial, because this permits evolution to adjust the number of active commands issued automatically. Adjusting local connectivity was identified as a beneficial feature in earlier research. The network induction method is tested on two tasks; finding a network that sustains an epidemic as long as possible and finding a network that, under simulation, closely matches a specified pattern of rise and fall in the number of infections., (Copyright © 2017 Elsevier B.V. All rights reserved.)

High-throughput methods have provided us with a large amount of data pertaining to protein-protein interaction networks. The alignment of these networks enables us to better understand biological systems. Given the fact that the alignment of networks is computationally intractable, it is important to introduce a more efficient and accurate algorithm which finds as large as possible similar areas among networks. This paper proposes a new algorithm named INDEX for the global alignment of protein-protein interaction networks. INDEX has multiple phases. First, it computes topological and biological scores of proteins and creates the initial alignment based on the proposed matching score strategy. Using networks topologies and aligned proteins, it then selects a set of high scoring proteins in each phase and extends new alignments around them until final alignment is obtained. Proposing a new alignment strategy, detailed consideration of matching scores, and growth of the alignment core has led INDEX to obtain a larger common connected subgraph with a much greater number of edges compared with previous methods. Regarding other measures such as edge correctness, symmetric substructure score, and runtime, the proposed algorithm performed considerably better than existing popular methods. Our results show that INDEX can be a promising method for identifying functionally conserved interactions., Availability: The INDEX executable file is available at https://github.com/a-mir/index/., (Copyright © 2017 Elsevier B.V. All rights reserved.)