Defining the players in higher-order networks: predictive modeling for reverse engineering functional influence networks.

ABSTRACT: Determining biological network dependencies that can help predict the behavior of a system given prior observations from high-throughput data is a very valuable but difficult task, especially in the light of the ever-increasing volume of experimental data. Such an endeavor can be greatly enhanced by considering regulatory influences on co-expressed groups of genes representing functional modules, thus constraining the number of parameters in the system. This allows development of network models that are predictive of system dynamics. We first develop a predictive network model of the transcriptomics of whole blood from a mouse model of neuroprotection in ischemic stroke, and show that it can accurately predict system behavior under novel conditions. We then use a network topology approach

SUBMITTER: McDermott JE 

PROVIDER: S-EPMC3052927 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature