Multi-omic data integration enables discovery of hidden biological regularities
Open accessAbstract
Rapid growth in size and complexity of biological data sets has led to the ‘Big Data to Knowledge’ challenge. We develop advanced data integration methods for multi-level analysis of genomic, transcriptomic, ribosomal profiling, proteomic and fluxomic data. First, we show that pairwise integration of primary omics data reveals regularities that tie cellular processes together in Escherichia coli: the number of protein molecules made per mRNA transcript and the number of ribosomes required per translated protein molecule. Second, we show that genome-scale models, based on genomic and bibliomic data, enable quantitative synchronization of disparate data types. Integrating omics data with models enabled the discovery of two novel regularities: condition invariant in vivo turnover rates of enzymes and the correlation of protein structural motifs and translational pausing. These regularities can be formally represented in a computable format allowing for coherent interpretation and prediction of fitness and selection that underlies cellular physiology.
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In genome-scale kinetic models of cellular metabolism, missing kcat values are typically addressed by sampling or fitting them to large datasets.
“In genome-scale kinetic models of cellular metabolism, this issue is typically addressed by either sampling missing k cat values or fitting them to large datasets 7 , 8 , 12 , 13 .”
From Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning (2023) · cites this paper as [8]
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Self-citation
- Authors
- Ebrahim, A., Brunk, E., Tan, J., O'Brien, E. J., Kim, D., Szubin, R., Lerman, J. A., Lechner, A., Sastry, A., Bordbar, A., Feist, A. M., & Palsson, B. O.
- Affiliations
- University of California San Diego (US)
- Novo Nordisk Foundation (DK)
- University of California, Berkeley (US)
- ORCID
- Venue
- Nature Communications
- Published
- 2016-10-26
- Volume
- 7
- Issue
- 1
- Pages
- 13091-13091
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC5095171
- OpenAlex
- W2541638185
- Cited by
- 155
- References
- 43
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 15 · 2023: 13 · 2022: 8 · 2021: 18 · 2020: 21 · 2019: 18 · 2018: 28 · 2017: 20 · 2016: 1
- FWCI
- 11.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by)
- Scholarworks@UNIST (Ulsan National Institute of Science and Technology) (submittedVersion, cc-by)
- Funders
- National Science Foundation (DEAC02-05CH11231)
- U.S. Department of Energy (-AC02-05CH11231, 05CH11231, DE AC02 05CH11231, DE-AC02-)
- National Energy Research Scientific Computing Center (DE–AC02–05CH11231, 05CH11231, AC02-05CH11231)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (DEAC02-05CH11231, 148961)
- Novo Nordisk (NNF16CC0021858)
- Novo Nordisk Fonden (NNF16CC0021858, NNF10CC1016517)
- National Institutes of Health (DE-AC02–05CH11231, NNF16CC0021858, NIH-R01)
- Office of Science (DEAC02–05CH11231, AC02-05CH11231, DE-AC02, 05CH11231)
- National Institute of General Medical Sciences (R01GM057089)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- multi-omics integration, Escherichia coli, ribosome profiling, proteomics, fluxomics, transcriptomics, genome-scale metabolic models, protein turnover rate, protein structural motifs, fitness prediction, cell physiology
- MeSH terms
- Bacterial Proteins, Enzymes, Escherichia coli, Models, Biological, Ribosomes, RNA, Bacterial, RNA, Messenger, Gene Expression Profiling, Proteomics, Datasets as Topic
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