The recent proliferation of """"""""omics"""""""" (genomic, transcriptomic, proteomic, and metabolic) data has necessitated a parallel expansion in the field of systems biology to meet the rising demands for analyzing this abundance of data. Constraints-based modeling, which has proven quite successful in recent years, is an approach whereby the allowable cellular phenotypes are calculated by elimination of infeasible behaviors through the successive imposition of constraints (stoichiometric, thermodynamic and flux capacity constraints). The R01 funding program begun in 1998 has enabled us to: 1) develop genome-scale constraints-based metabolic models for E. coli, S. cerevisiae, H. influenzae, and H. pytori; 2) develop methods to analyze these metabolic networks; 3) develop methods for modeling regulation, including building a core regulatory model for E. coli; and 4) develop methods for the sequenced-based analysis of metabolic demands for transcription and translation. The proposed work presented here will build on our previous accomplishments. We propose to:1.) Expand our E. coli metabolic model to include more E. coli metabolic gene products as they become characterized, and to expand our E. coli regulatory and transcription and translation models to genome-scale. II) Integrate these three components, resulting in a comprehensive model describing metabolism, regulation, transcription, and translation and will account for roughly 2000 of the 4,401 open reading frames found in E. coli K-12 MG1655. This integrated model will then be used to develop a data-driven model-centric database, where """"""""omics"""""""" data can place additional constraints on the allowable solution space and can be used to calculate biological parameters. The database will also provide predictions in silico of transcriptomic, proteomic, metabolomic, and phenomic data for prospective experimental design. if the proposed program is approved and implemented, we will construct and test the most comprehensive single cell model ever. This in silico model should serve as a basis for generating highly focused hypotheses and should be extendable to other sequenced strains of E. coli.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM057089-07
Application #
6889899
Study Section
Special Emphasis Panel (ZRG1-SSS-H (90))
Program Officer
Jones, Warren
Project Start
1998-08-01
Project End
2007-04-30
Budget Start
2005-05-01
Budget End
2006-04-30
Support Year
7
Fiscal Year
2005
Total Cost
$380,000
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
Brunk, Elizabeth; Chang, Roger L; Xia, Jing et al. (2018) Characterizing posttranslational modifications in prokaryotic metabolism using a multiscale workflow. Proc Natl Acad Sci U S A 115:11096-11101
Latif, Haythem; Federowicz, Stephen; Ebrahim, Ali et al. (2018) ChIP-exo interrogation of Crp, DNA, and RNAP holoenzyme interactions. PLoS One 13:e0197272
Brunk, Elizabeth; Sahoo, Swagatika; Zielinski, Daniel C et al. (2018) Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 36:272-281
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
Yurkovich, James T; Yang, Laurence; Palsson, Bernhard O (2017) Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells. PLoS Comput Biol 13:e1005424
Monk, Jonathan M; Lloyd, Colton J; Brunk, Elizabeth et al. (2017) iML1515, a knowledgebase that computes Escherichia coli traits. Nat Biotechnol 35:904-908
Sastry, Anand; Monk, Jonathan; Tegel, Hanna et al. (2017) Machine learning in computational biology to accelerate high-throughput protein expression. Bioinformatics 33:2487-2495
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
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
Brunk, Elizabeth; Mih, Nathan; Monk, Jonathan et al. (2016) Systems biology of the structural proteome. BMC Syst Biol 10:26

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