Background: Established in 1998, this R01 program has led the development of genome-scale reconstructions of bacterial metabolism, with special emphasis on E. coli. These reconstructions have been disseminated to the research community at large and led to the generation of many scientific studies from laboratories around the world. These genome-scale metabolic reconstructions are turning out to be a common denominator in the study of systems biology in microbes. Proposed Program: This continuation proposal outlines a program with two specific aims.
The first aim i s to continue to develop reconstruction technologies and expand the scope of the networks currently being reconstructed to include, on a genome-scale, metabolism, regulation of gene expression, including two- component signaling, as well as transcription and translation. The fulfillment of this aim will result in the largest and most comprehensive network reconstruction for any organism.
The second aim i s to use the metabolic reconstruction, which now comprehensively represents known metabolism in E. coli, for prospective discovery of new metabolic capabilities in E. coli. Such discovery is accomplished through a gap filling procedure that follows high-throughput phenotyping experiments of wild-type and knock-out strains.
This second aim i s novel and represents fulfillment of the promise of systems biology to systematically discover new biological functions through integrative computational and experimental studies. General Impact: This program, if renewed, will continue to pioneer the new genome-scale science of prokaryotic cells. It will lay the foundation for similar analysis of human pathogens, environmentally important organisms and those of bioterrorism importance. The results will thus have an impact on both basic and applied studies of bacteria.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM057089-12
Application #
7808884
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Jones, Warren
Project Start
1998-08-01
Project End
2011-04-30
Budget Start
2010-05-01
Budget End
2011-04-30
Support Year
12
Fiscal Year
2010
Total Cost
$390,283
Indirect Cost
Name
University of California San Diego
Department
Engineering (All Types)
Type
Schools of Arts and Sciences
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
Fang, Xin; Sastry, Anand; Mih, Nathan et al. (2017) Global transcriptional regulatory network for Escherichia coli robustly connects gene expression to transcription factor activities. Proc Natl Acad Sci U S A 114:10286-10291
Chen, Ke; Gao, Ye; Mih, Nathan et al. (2017) Thermosensitivity of growth is determined by chaperone-mediated proteome reallocation. Proc Natl Acad Sci U S A 114:11548-11553
Yang, Laurence; Yurkovich, James T; Lloyd, Colton J et al. (2016) Principles of proteome allocation are revealed using proteomic data and genome-scale models. Sci Rep 6:36734
Brunk, Elizabeth; Mih, Nathan; Monk, Jonathan et al. (2016) Systems biology of the structural proteome. BMC Syst Biol 10:26
Monk, Jonathan M; Koza, Anna; Campodonico, Miguel A et al. (2016) Multi-omics Quantification of Species Variation of Escherichia coli Links Molecular Features with Strain Phenotypes. Cell Syst 3:238-251.e12
Yang, Laurence; Ma, Ding; Ebrahim, Ali et al. (2016) solveME: fast and reliable solution of nonlinear ME models. BMC Bioinformatics 17:391
Feist, Adam M; Palsson, Bernhard O (2016) What do cells actually want? Genome Biol 17:110
Brunk, Elizabeth; George, Kevin W; Alonso-Gutierrez, Jorge et al. (2016) Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow. Cell Syst 2:335-46
Utrilla, Jose; O'Brien, Edward J; Chen, Ke et al. (2016) Global Rebalancing of Cellular Resources by Pleiotropic Point Mutations Illustrates a Multi-scale Mechanism of Adaptive Evolution. Cell Syst 2:260-71
Ebrahim, Ali; Brunk, Elizabeth; Tan, Justin et al. (2016) Multi-omic data integration enables discovery of hidden biological regularities. Nat Commun 7:13091

Showing the most recent 10 out of 88 publications