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Network Component Analysis of Nitric Oxide Challenge to Escherichia Coli

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Air date: Tuesday, July 10, 2007, 2:00:00 PM
Time displayed is Eastern Time, Washington DC Local
Views: Total views: 114 * This only includes stats from October 2011 and forward.
Category: Special
Runtime: 01:09:49
Description: Systems Biology Special Interest Group

Traditional methods for analyzing genomic scale expression data ignore the underlying network structures and provide inferences based purely on statistical constraints of the computed signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. We develop a method, called network component analysis (NCA), for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. This analysis, combined with other biochemical and genetic techniques, was used to deduce the direct nitric oxide (NO) targets in E. coli and the associated response networks.

NCA was used to identify transcription factors that are perturbed by NO. Such information was screened with potential NO reaction mechanisms and phenotypical data from genetic knockouts to identify active chemistry and direct NO targets in E. coli. This approach identified the comprehensive E. coli NO response network and evinced that NO halts bacterial growth via inhibition of branched-chain amino acid biosynthesis enzyme dihydroxyacid dehydratase.

Dr. Liao obtained his B.S. from National Taiwan University in 1980; Ph.D. at the University of Wisconsin-Madison in 1987. He received the National Science Foundation Presidential Young Investigator Award in 1992 and became a Fellow of the American Institute for Medical and Biological Engineering in 2002. He has been selected for the W.N. Lacey Lectureship in Chemical Engineering at California Institute of Technology and received the Food, Pharmaceutical and Bioengineering Division Award of the American Institute of Chemical Engineers and the Merck Award in Metabolic Engineering, 2006. Dr. Liao is also associated with the UCLA-DOE Institute for Genomics and Proteomics; UCLA-NASA Institute for Cell Mimetic Space Exploration and the California NanoSystems Institute.

http://www.seas.ucla.edu/~liaoj

http://www.nih.gov/sigs/sysbio
NLM Title: Network component analysis of nitric oxide challenge to Escherichia coli / James Liao.
Author: Liao, James.
National Institutes of Health (U.S.). Systems Biology Special Interest Group.
Publisher:
Abstract: (CIT): Systems Biology Special Interest Group Traditional methods for analyzing genomic scale expression data ignore the underlying network structures and provide inferences based purely on statistical constraints of the computed signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. We develop a method, called network component analysis (NCA), for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. This analysis, combined with other biochemical and genetic techniques, was used to deduce the direct nitric oxide (NO) targets in E. coli and the associated response networks. NCA was used to identify transcription factors that are perturbed by NO. Such information was screened with potential NO reaction mechanisms and phenotypical data from genetic knockouts to identify active chemistry and direct NO targets in E. coli. This approach identified the comprehensive E. coli NO response network and evinced that NO halts bacterial growth via inhibition of branched-chain amino acid biosynthesis enzyme dihydroxyacid dehydratase.
Subjects: Escherichia coli
Gene Expression Regulation, Bacterial
Gene Regulatory Networks
Nitric Oxide--metabolism
Transcription Factors
Publication Types: Lecture
Webcast
NLM Classification: QW 51
NLM ID: 101313562
CIT Live ID: 5997
Permanent link: https://videocast.nih.gov/watch=5997

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