Final Technical Report

[摘要] Most prokaryotes of interest to DOE are poorly understood. Even when full genomic sequences are available, the function of only a small number of gene products are clear. The critical question is how to best infer the most probable network architectures in cells that are poorly characterized. The project goal is to create a computational hypothesis testing (CHT) framework that combines large-scale dynamical simulation, a database of bioinformatics-derived probable interactions, and numerical parallel architecture data-fitting routines to explore many “what if ?” hypotheses about the functions of genes and proteins within pathways and their downstream effects on molecular concentration profiles and corresponding phenotypes. From this framework we expect to infer signal transduction pathways and gene expression networks in prokaryotes. Detailed mechanistic models of E. Coli have been developed that directly incorporate DNA sequence information. The CHT framework is implemented in the NIEngine network inference software. NIEngine has been applied to recover gene regulatory networks in E. coli to assess performance. Application to Shewanel la oneidensi and other organism of interest DOE will be conducted in partnership with Jim Collin's Lab at Boston University and other academic partners. The CHT framework has also found broad application in the automated learning of biology for purposes of improving human health.

[发布日期] 2008-10-15

[学科分类] 生物科学（综合）