BioSystems 

About: BioSystems is an academic journal published by Elsevier BV. The journal publishes majorly in the area(s): Population & Medicine. It has an ISSN identifier of 0303-2647. Over the lifetime, 3950 publications have been published receiving 89287 citations. The journal is also known as: Bio systems.

Ant colonies for the travelling salesman problem

Abstract: We describe an artificial ant colony capable of solving the travelling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer simulations demonstrate that the artificial ant colony is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks and evolutionary computation, of the successful use of a natural metaphor to design an optimization algorithm.

Coevolutionary games--a mini review.

Abstract: Prevalence of cooperation within groups of selfish individuals is puzzling in that it contradicts with the basic premise of natural selection. Favoring players with higher fitness, the latter is key for understanding the challenges faced by cooperators when competing with defectors. Evolutionary game theory provides a competent theoretical framework for addressing the subtleties of cooperation in such situations, which are known as social dilemmas. Recent advances point towards the fact that the evolution of strategies alone may be insufficient to fully exploit the benefits offered by cooperative behavior. Indeed, while spatial structure and heterogeneity, for example, have been recognized as potent promoters of cooperation, coevolutionary rules can extend the potentials of such entities further, and even more importantly, lead to the understanding of their emergence. The introduction of coevolutionary rules to evolutionary games implies, that besides the evolution of strategies, another property may simultaneously be subject to evolution as well. Coevolutionary rules may affect the interaction network, the reproduction capability of players, their reputation, mobility or age. Here we review recent works on evolutionary games incorporating coevolutionary rules, as well as give a didactic description of potential pitfalls and misconceptions associated with the subject. In addition, we briefly outline directions for future research that we feel are promising, thereby particularly focusing on dynamical effects of coevolutionary rules on the evolution of cooperation, which are still widely open to research and thus hold promise of exciting new discoveries.

Autopoiesis: The Organization of Living Systems, Its Characterization and a Model

Abstract: Notwithstanding their diversity, all living systems must share a common organization which we implicitly recognize calling them “living.” At present there is no formulation of this organization, mainly because the great developments of molecular, genetic and evolutionary notions in contemporary biology have led to the overemphasis of isolated components, e.g., to consider reproduction as a necessary feature of the living organization and, hence, not to ask about the organization which makes a living system a whole, autonomous unity that is alive regardless of whether it reproduces or not. As a result, processes that are history dependent (evolution, ontogenesis) and history independent (individual organization) have been confused in the attempt to provide a single mechanistic explanation for phenomena which, although related, are fundamentally distinct.

Gene regulatory network inference: Data integration in dynamic models—A review

Abstract: Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.

CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks.

Abstract: Background and scope Nowadays, centrality analysis has become a principal method for identifying essential proteins in biological networks. Here we present CytoNCA, a Cytoscape plugin integrating calculation, evaluation and visualization analysis for multiple centrality measures. Implementation and performance (i) CytoNCA supports eight different centrality measures and each can be applied to both weighted and unweighted biological networks. (ii) It allows users to upload biological information of both nodes and edges in the network, to integrate biological data with topological data to detect specific nodes. (iii) CytoNCA offers multiple potent visualization analysis modules, which generate various forms of output such as graph, table, and chart, and analyze associations among all measures. (iv) It can be utilized to quantitatively assess the calculation results, and evaluate the accuracy by statistical measures. (v) Besides current eight centrality measures, the biological characters from other sources could also be analyzed and assessed by CytoNCA. This makes CytoNCA an excellent tool for calculating centrality, evaluating and visualizing biological networks. Availability http://apps.cytoscape.org/apps/cytonca .