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Multi-omic data integration enables discovery of hidden biological regularities

Author

Listed:
  • 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
    The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark)

  • 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
    The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark)

  • Bernhard O. Palsson

    (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
    Bioinformatics and Systems Biology Program, University of California
    University of California)

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.

Suggested Citation

  • 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 & Bernhard O. Palsson, 2016. "Multi-omic data integration enables discovery of hidden biological regularities," Nature Communications, Nature, vol. 7(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13091
    DOI: 10.1038/ncomms13091
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    Cited by:

    1. Alexander Kroll & Yvan Rousset & Xiao-Pan Hu & Nina A. Liebrand & Martin J. Lercher, 2023. "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Arjun Patel & Dominic McGrosso & Ying Hefner & Anaamika Campeau & Anand V. Sastry & Svetlana Maurya & Kevin Rychel & David J. Gonzalez & Bernhard O. Palsson, 2024. "Proteome allocation is linked to transcriptional regulation through a modularized transcriptome," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Guido Zampieri & Supreeta Vijayakumar & Elisabeth Yaneske & Claudio Angione, 2019. "Machine and deep learning meet genome-scale metabolic modeling," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-24, July.

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