We're interested in networks. A network is a group of entities, connected by a web of interactions, whose collective behavior can be remarkably complex. In living systems, networks arise in various contexts, across many scales. Physical networks of specific amino-acid contacts can generate an attractive potential between proteins. The set of transient and permanent protein complexes in a cell is abstractly represented as a protein interaction network. The uptake and efflux of material by a cell, and the movement of cargo between various intracellular compartments, defines a traffic network. Cells use signalling and regulatory networks to control gene expression, metabolism, and traffic, in response to external conditions. And groups of cells can influence one-another, as occurs by means of chemical and electrical signals in a neuronal network.
In every sense, these are truly living networks. Each network is a dynamic object, whose state changes as a function of time; moreover, the structure of a network itself, its connectivity and topology, can vary over evolutionary timescales. Together, these aspects define the two questions on which we focus. First, we use quantitative experiments, coupled with mathematical and computational models, to study the dynamical properties of living networks. Second, we use genomic and protein sequence data to probe the evolutionary history of these systems. We find that, across scales and contexts, the ‘network idea’ is a broad and powerful framework within which complexity of biological systems can be usefully organized and studied.
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Thattai, M., Burak, Y., and Shraiman, B. I. (2007) The origins of specificity in polyketide synthase protein interactions. PLoS Computational Biology 3, e186.
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