Development of bottom-up biochemically and genetically structured network reconstructions in conjunction with models of the major cellular processes of E. coli K-12 MG1655 has been accomplished over the last twelve years with this research program. Metabolism, transcriptional regulation, and transcription/translation interactions have been illuminated. Building of component-level knowledge has provided the common theme that allowed for the analysis of individual component functions within the context of the entire cellular system. The resulting network reconstructions and in silico models are now in wide use in the scientific community. This program proposes continuation built around three specific aims. First, additional cellular processes targeted for continuing and new reconstruction;including metabolism, transcription &translation, transcriptional regulation and two-component signaling. This is a continuation of previous work. Second, we propose the reconstruction of the protein interaction network in E. coli and its integration with the networks under aim #1, that includes, protein complex formation, the kinase and phosphatase networks coupled with their associated targets, and the protein- protein interaction networks. This is a new and novel effort. Third, we will map the E. coli K-12 MG1655 networks onto pathogenic E. coli strains molecular. Predictive power to suggest optimal nutritional conditions for colonization of host tissues and suppressive pathogenic strains growth conditions will also be addressed by the reconstructions. Accomplishment of research aims will lead to significant improvements in computational organismal models. Importance of these reconstructions is demonstrated by the current extensive use of these reconstructions models by the scientific community. Integrated networks will provide an expanded """"""""container"""""""" within which the scientific community""""""""s """"""""-omics"""""""" data can be analyzed revealing an even more detailed insight into workings of cellular function. Final application is the development of reconstructions for pathogenic strains to provide a complete and accurate framework for modeling bacterial pathogenesis.

Public Health Relevance

Biochemically, genomically, and genetically structured reaction networks of the major cellular processes in the model organism E. coli K-12 MG1655 have been the focus of this 12-year old program. The resulting computer models that describe these cellular functions are now in worldwide use to address a number of basic and applied problems in microbiology. The continuation of this program focuses on increasing the number of genes represented in the computer models to 2/3rds of the genes expressed to increase their accuracy and use, and to build corresponding computer models of pathogenic strains of E. coli.

National Institute of Health (NIH)
National Institute of General Medical Sciences (NIGMS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Gerratana, Barbara
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University of California San Diego
Engineering (All Types)
Schools of Arts and Sciences
La Jolla
United States
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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
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
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
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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