Modeling information flow in biological networks

National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA.
Physical Biology (Impact Factor: 3.14). 06/2011; 8(3):035012. DOI: 10.1088/1478-3975/8/3/035012
Source: PubMed

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.

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Available from: Teresa Przytycka, Jul 07, 2015
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    • "Numerous approaches for analyzing biological networks based on information flow [1] [2] [3] [4] [5] [6] [7] have emerged in recent years. The main assumption of all such methods is information transitivity: information can flow through or can be exchanged via paths of biological interactions. "
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