title: Multi-omic data integration enables discovery of hidden biological regularities.
abstract: 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.
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.
authors: Ali
authors: A
authors: Ebrahim
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220 DK-2800 Kongens Lyngby, Denmark.
authors: Elizabeth
authors: E
authors: Brunk
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.
authors: Justin
authors: J
authors: Tan
authors: Bioinformatics and Systems Biology Program, University of California, San Diego, California 92093, USA.
authors: Edward J
authors: EJ
authors: O'Brien
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.
authors: Donghyuk
authors: D
authors: Kim
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.
authors: Richard
authors: R
authors: Szubin
authors: Bioinformatics and Systems Biology Program, University of California, San Diego, California 92093, USA.
authors: Joshua A
authors: JA
authors: Lerman
authors: Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, USA.
authors: Anna
authors: A
authors: Lechner
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.
authors: Anand
authors: A
authors: Sastry
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.
authors: Aarash
authors: A
authors: Bordbar
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220 DK-2800 Kongens Lyngby, Denmark.
authors: Adam M
authors: AM
authors: Feist
authors: Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA.|The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220 DK-2800 Kongens Lyngby, Denmark.|Bioinformatics and Systems Biology Program, University of California, San Diego, California 92093, USA.|Department of Pediatrics, University of California, San Diego, California 92093, USA.
authors: Bernhard O
authors: BO
authors: Palsson
mesh_terms: D001426:Bacterial Proteins / Q000235:genetics / Q000378:metabolism; D066264:Datasets as Topic; D004798:Enzymes / Q000378:metabolism; D004926:Escherichia coli / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D040901:Proteomics / Q000379:methods*; D012329:RNA, Bacterial / Q000235:genetics / Q000378:metabolism; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism; D012270:Ribosomes / Q000235:genetics / Q000378:metabolism
chemical_list: D001426:Bacterial Proteins; D004798:Enzymes; D012329:RNA, Bacterial; D012333:RNA, Messenger