title: Modeling information flow in biological networks.
abstract: Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method.
authors.affiliation: National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA. kimy3@ncbi.nlm.nih.gov
authors.forename: Yoo-Ah
authors.initials: YA
authors.lastname: Kim
authors.forename: Jozef H
authors.initials: JH
authors.lastname: Przytycki
authors.forename: Stefan
authors.initials: S
authors.lastname: Wuchty
authors.forename: Teresa M
authors.initials: TM
authors.lastname: Przytycka
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D005838:Genotype; D006801:Humans; D008954:Models, Biological; D010641:Phenotype; D011485:Protein Binding; D060066:Protein Interaction Maps / Q000235:genetics / Q000502:physiology*