72 research outputs found

    Mean-field theory for scale-free random networks

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    Random networks with complex topology are common in Nature, describing systems as diverse as the world wide web or social and business networks. Recently, it has been demonstrated that most large networks for which topological information is available display scale-free features. Here we study the scaling properties of the recently introduced scale-free model, that can account for the observed power-law distribution of the connectivities. We develop a mean-field method to predict the growth dynamics of the individual vertices, and use this to calculate analytically the connectivity distribution and the scaling exponents. The mean-field method can be used to address the properties of two variants of the scale-free model, that do not display power-law scaling.Comment: 19 pages, 6 figure

    Bacteriophage-mediated competition in Bordetella bacteria

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    Apparent competition between species is believed to be one of the principle driving forces that structure ecological communities, although the precise mecha nisms have yet to be characterized. Here we develop a model system that isolates phage-mediated interactions by neutralizing resource competition using two genetically identical Bordetella bronchiseptica strains that differ only in that one is the carrier of a phage and the other is susceptible to the phage. We observe and quantify the competitive advantage of the bacterial strain bearing the prophage in both invading and in resisting invasion by bacteria susceptible to the phage, and use our measurements to develop a mathematical model of phage-mediated competition. The model predicts, and experimental evidence confirms, that the competitive advantage conferred by the phage depends only on the relative phage pathology and is independent of other phage and host parameters. This work combines experimental and mathematical approaches to the study of phage-driven competition, and provides an experimentally tested framework for evaluation of the effects of pathogens/parasites on interspecific competition.Comment: 10pages, 8 figure

    Some Perspectives on Network Modeling in Therapeutic Target Prediction

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    Drug target identification is of significant commercial interest to pharmaceutical companies, and there is a vast amount of research done related to the topic of therapeutic target identification. Interdisciplinary research in this area involves both the biological network community and the graph algorithms community. Key steps of a typical therapeutic target identification problem include synthesizing or inferring the complex network of interactions relevant to the disease, connecting this network to the disease-specific behavior, and predicting which components are key mediators of the behavior. All of these steps involve graph theoretical or graph algorithmic aspects. In this perspective, we provide modelling and algorithmic perspectives for therapeutic target identification and highlight a number of algorithmic advances, which have gotten relatively little attention so far, with the hope of strengthening the ties between these two research communities

    Transience and constancy of interactions in a plant-frugivore network

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    Plant-animal mutualistic interactions such as frugivory and seed dispersal display great variation in time due to fluctuations in fruit abundance, animal abundance, and behavior. In particular, some species participate in interactions with other species only transiently, while other species are active for longer periods of time. Species with a longer period of activity are able to interact with more species, and thus engage in constant participation in an interaction network. Species with high constancy would thus be expected to help maintain the biodiversity of a community; however, the manner in which constant species link to their partners may be critical to species coexistence. Because species that interact with many partners concurrently could create more competition compared to those species that interact sequentially with many partners, evaluating the concurrence in an interaction network sheds light on how the network can maintain biodiversity. In this study, we investigate how phenological patterns of fruit production and frugivore presence affect the temporal variation of a plant-frugivore network, and focus on the manner in which high degree species collect their interactions over time. We found a clear separation of activity periods: most species appeared only briefly and participated in relatively few interactions, or showed activity for longer time periods and participated in more interactions. Species that were active for longer time periods often shifted interactions, resulting in a sequential collection of their partners in time, rather than concurrence. For the seed dispersal mutualism in particular, sequential accumulation of partners may allow plant species more opportunities to disperse their seeds compared to concurrence. We suggest that for temporally and spatially heterogeneous landscapes, sequential accumulation of partners would serve to reduce competition and facilitate coexistence of species. Copyright © 2013 Yang et al
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