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.
| 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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