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 Feist, Bernhard Palsson

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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.
Original languageEnglish
Article number13091
JournalNature Communications
Volume7
Number of pages9
ISSN2041-1723
DOIs
Publication statusPublished - 2016

Bibliographical note

This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Keywords

  • Biochemical networks
  • Computer modelling
  • Systems analysis

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