Biology – Quantitative Biology – Molecular Networks
Scientific paper
2011-12-16
J. Comput. Biol., 14 (8): 1115-1143, 2007
Biology
Quantitative Biology
Molecular Networks
30 pages, 5 figures. This paper was published in 2007 in Journal of Computational Biology. The version posted here does not in
Scientific paper
10.1089/cmb.2007.0069
Interaction networks, consisting of agents linked by their interactions, are ubiquitous across many disciplines of modern science. Many methods of analysis of interaction networks have been proposed, mainly concentrating on node degree distribution or aiming to discover clusters of agents that are very strongly connected between themselves. These methods are principally based on graph-theory or machine learning. We present a mathematically simple formalism for modelling context-specific information propagation in interaction networks based on random walks. The context is provided by selection of sources and destinations of information and by use of potential functions that direct the flow towards the destinations. We also use the concept of dissipation to model the aging of information as it diffuses from its source. Using examples from yeast protein-protein interaction networks and some of the histone acetyltransferases involved in control of transcription, we demonstrate the utility of the concepts and the mathematical constructs introduced in this paper.
Stojmirović Aleksandar
Yu Yi-Kuo
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