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Online ISSN 1751-8857 Print ISSN 1751-8849

IET Systems Biology

Volume 4, Issue 3, May 2010

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Volume 4, Issue 3

May 2010

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    • Effect of swarming on biodiversity in non-symmetric rock–paper–scissor game
      • Author(s): R. Bose
      • Source: IET Systems Biology, Volume 4, Issue 3, p. 177 –184
      • DOI:  10.1049/iet-syb.2008.0170
      • Type: Article
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      • p. 177 –184 (8)
        Cyclic dominance of species is a potential mechanism for maintaining biodiversity. The author investigates the generalised scenario when the cyclic dominance of three or more interacting species is described by a non-symmetric matrix game that has multiple Nash equilibria. Modified Lotka–Volterra equations are proposed to incorporate the effects of swarming, and the condition for biodiversity is derived. The species are modelled using replicator equations, where each member of the species is assigned a fitness value. The authors show, for the first time, that the ‘swarming effect’ has an important role to play in the maintenance of biodiversity. The authors have also discovered the existence of a critical value of the swarming parameter for a given mobility, above which there is a high probability of existence of biodiversity.
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      Application of graph colouring to biological networks
      • Author(s): S. Khor
      • Source: IET Systems Biology, Volume 4, Issue 3, p. 185 –192
      • DOI:  10.1049/iet-syb.2009.0038
      • Type: Article
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      • p. 185 –192 (8)
        The author explores the application of graph colouring to biological networks, specifically protein–protein interaction (PPI) networks. First, the author finds that given similar conditions (i.e. graph size, degree distribution and clustering), fewer colours are needed to colour disassortative than assortative networks. Fewer colours create fewer independent sets which in turn imply higher concurrency potential for a network. Since PPI networks tend to be disassortative, the author suggests that in addition to functional specificity and stability proposed previously by Maslov and Sneppen (Science, 296, 2002), the disassortative nature of PPI networks may promote the ability of cells to perform multiple, crucial and functionally diverse tasks concurrently. Second, because graph colouring is closely related to the presence of cliques in a graph, the significance of node colouring information to the problem of identifying protein complexes (dense subgraphs in PPI networks), is investigated. The author finds that for PPI networks where 1–11% of nodes participate in at least one identified protein complex, such as H. sapien, DSATUR (a well-known complete graph colouring algorithm) node colouring information can improve the quality (homogeneity and separation) of initial candidate complexes. This finding may help improve existing protein complex detection methods, and/or suggest new methods. [Includes supplementary material]
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      Approximating stochastic biochemical processes with Wasserstein pseudometrics
      • Author(s): D. Thorsley  and  E. Klavins
      • Source: IET Systems Biology, Volume 4, Issue 3, p. 193 –211
      • DOI:  10.1049/iet-syb.2009.0039
      • Type: Article
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      • p. 193 –211 (19)
        Modelling stochastic processes inside the cell is difficult due to the size and complexity of the processes being investigated. As a result, new approaches are needed to address the problems of model reduction, parameter estimation, model comparison and model invalidation. Here, the authors propose addressing these problems by using Wasserstein pseudometrics to quantify the differences between processes. The method the authors propose is applicable to any bounded continuous-time stochastic process and pseudometrics between processes are defined only in terms of the available outputs. Algorithms for approximating Wasserstein pseudometrics are developed from experimental or simulation data and demonstrate how to optimise parameter values to minimise the pseudometrics. The approach is illustrated with studies of a stochastic toggle switch and of stochastic gene expression in E. coli.
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      Prioritisation of associations between protein domains and complex diseases using domain–domain interaction networks
      • Author(s): W. Wang ; W. Zhang ; R. Jiang ; Y. Luan
      • Source: IET Systems Biology, Volume 4, Issue 3, p. 212 –222
      • DOI:  10.1049/iet-syb.2009.0037
      • Type: Article
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      • p. 212 –222 (11)
        It is of vital importance to find genetic variants that underlie human complex diseases and locate genes that are responsible for these diseases. Since proteins are typically composed of several structural domains, it is reasonable to assume that harmful genetic variants may alter structures of protein domains, affect functions of proteins and eventually cause disorders. With this understanding, the authors explore the possibility of recovering associations between protein domains and complex diseases. The authors define associations between protein domains and disease families on the basis of associations between non-synonymous single nucleotide polymorphisms (nsSNPs) and complex diseases, similarities between diseases, and relations between proteins and domains. Based on a domain–domain interaction network, the authors propose a ‘guilt-by-proximity’ principle to rank candidate domains according to their average distance to a set of seed domains in the domain–domain interaction network. The authors validate the method through large-scale cross-validation experiments on simulated linkage intervals, random controls and the whole genome. Results show that areas under receiver operating characteristic curves (AUC scores) can be as high as 77.90%, and the mean rank ratios can be as low as 21.82%. The authors further offer a freely accessible web interface for a genome-wide landscape of associations between domains and disease families.
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      Determining the distance to monotonicity of a biological network: a graph-theoretical approach
      • Author(s): G. Iacono ; F. Ramezani ; N. Soranzo ; C. Altafini
      • Source: IET Systems Biology, Volume 4, Issue 3, p. 223 –235
      • DOI:  10.1049/iet-syb.2009.0040
      • Type: Article
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      • p. 223 –235 (13)
        The authors use ideas from graph theory in order to determine how distant is a given biological network from being monotone. On the signed graph representing the system, the minimal number of sign inconsistencies (i.e. the distance to monotonicity) is shown to be equal to the minimal number of fundamental cycles having a negative sign. Suitable operations aiming at computing such a number are also proposed and shown to outperform all algorithms that are so far existing for this task. [Includes supplementary material]
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      Multi-objective mixed integer strategy for the optimisation of biological networks
      • Author(s): J.O.H. Sendín ; O. Exler ; J.R. Banga
      • Source: IET Systems Biology, Volume 4, Issue 3, p. 236 –248
      • DOI:  10.1049/iet-syb.2009.0045
      • Type: Article
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      • p. 236 –248 (13)
        In this contribution, the authors consider multi-criteria optimisation problems arising from the field of systems biology when both continuous and integer decision variables are involved. Mathematically, they are formulated as mixed-integer non-linear programming problems. The authors present a novel solution strategy based on a global optimisation approach for dealing with this class of problems. Its usefulness and capabilities are illustrated with two metabolic engineering case studies. For these problems, the authors show how the set of optimal solutions (the so-called Pareto front) is successfully and efficiently obtained, providing further insight into the systems under consideration regarding their optimal manipulation.
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