Information flow in interaction networks.
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
A
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA.
Yu
Yi-Kuo
YK
D019295:Computational Biology; D051548:Histone Acetyltransferases / Q000378:metabolism; D007257:Information Theory; D008390:Markov Chains; D008433:Mathematics; D008954:Models, Biological; D008962:Models, Theoretical; D025941:Protein Interaction Mapping; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism; D014157:Transcription Factors / Q000378:metabolism
D029701:Saccharomyces cerevisiae Proteins; D014157:Transcription Factors; D051548:Histone Acetyltransferases