title: Information flow in interaction networks II: channels, path lengths, and potentials.
abstract: In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by constructing a potential function facilitating a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow. Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework.
authors.affiliation: National Central for Biotechnology Information, National Library of Medicine, National Institute of Health, Bethesda, Maryland, USA.
authors.forename: Aleksandar
authors.initials: A
authors.lastname: Stojmirović
authors.forename: Yi-Kuo
authors.initials: YK
authors.lastname: Yu
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D008390:Markov Chains; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D010675:Pheromones / Q000378:metabolism*; D011336:Probability; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000378:metabolism*
chemical_list: D010675:Pheromones; D029701:Saccharomyces cerevisiae Proteins