72 research outputs found
Mean-field theory for scale-free random networks
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
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
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
Introduction to the Special Issue on Approaches to Control Biological and Biologically Inspired Networks
The emerging field at the intersection of quantitative biology, network
modeling, and control theory has enjoyed significant progress in recent years.
This Special Issue brings together a selection of papers on complementary
approaches to observe, identify, and control biological and biologically
inspired networks. These approaches advance the state of the art in the field
by addressing challenges common to many such networks, including high
dimensionality, strong nonlinearity, uncertainty, and limited opportunities for
observation and intervention. Because these challenges are not unique to
biological systems, it is expected that many of the results presented in these
contributions will also find applications in other domains, including physical,
social, and technological networks
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