Multi-omic data integration enables discovery of hidden biological regularities

Ali Ebrahim, Elizabeth Brunk, Justin Tan, Edward J. O'Brien, Donghyuk Kim, Richard Szubin, Joshua A. Lerman, Anna Lechner, Anand Sastry, Aarash Bordbar, Adam M. Feist and Bernhard O. Palsson ()
Additional contact information
Ali Ebrahim: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Elizabeth Brunk: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Justin Tan: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Edward J. O'Brien: Bioinformatics and Systems Biology Program, University of California
Donghyuk Kim: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Richard Szubin: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Joshua A. Lerman: Bioinformatics and Systems Biology Program, University of California
Anna Lechner: University of California
Anand Sastry: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Aarash Bordbar: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Adam M. Feist: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA
Bernhard O. Palsson: University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, California 92093, USA

Nature Communications, 2016, vol. 7, issue 1, 1-9

Abstract: 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.

Date: 2016
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/ncomms13091 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13091

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/ncomms13091

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().