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In silico method for modelling metabolism and gene product expression at genome scale

Author

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  • Joshua A. Lerman

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

  • Daniel R. Hyduke

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

  • Haythem Latif

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

  • Vasiliy A. Portnoy

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

  • Nathan E. Lewis

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

  • Jeffrey D. Orth

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

  • Alexandra C. Schrimpe-Rutledge

    (Pacific Northwest National Laboratory)

  • Richard D. Smith

    (Pacific Northwest National Laboratory)

  • Joshua N. Adkins

    (Pacific Northwest National Laboratory)

  • Karsten Zengler

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

  • Bernhard O. Palsson

    (University of California–San Diego, PFBH Room 419, 9500 Gliman Drive, La Jolla, California 92093-0412, USA.)

Abstract

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome and transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.

Suggested Citation

  • Joshua A. Lerman & Daniel R. Hyduke & Haythem Latif & Vasiliy A. Portnoy & Nathan E. Lewis & Jeffrey D. Orth & Alexandra C. Schrimpe-Rutledge & Richard D. Smith & Joshua N. Adkins & Karsten Zengler & , 2012. "In silico method for modelling metabolism and gene product expression at genome scale," Nature Communications, Nature, vol. 3(1), pages 1-10, January.
    • Handle: RePEc:nat:natcom:v:3:y:2012:i:1:d:10.1038_ncomms1928
    DOI: 10.1038/ncomms1928
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    Cited by:

    1. Robert Planqué & Josephus Hulshof & Bas Teusink & Johannes C Hendriks & Frank J Bruggeman, 2018. "Maintaining maximal metabolic flux by gene expression control," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-20, September.
    2. Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    3. Ambros M. Gleixner & Daniel E. Steffy & Kati Wolter, 2016. "Iterative Refinement for Linear Programming," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 449-464, August.
    4. 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.
    5. Hao Leng & Yinzhao Wang & Weishu Zhao & Stefan M. Sievert & Xiang Xiao, 2023. "Identification of a deep-branching thermophilic clade sheds light on early bacterial evolution," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    6. Iván Domenzain & Benjamín Sánchez & Mihail Anton & Eduard J. Kerkhoven & Aarón Millán-Oropeza & Céline Henry & Verena Siewers & John P. Morrissey & Nikolaus Sonnenschein & Jens Nielsen, 2022. "Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    7. 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.
    8. Christian Schulz & Tjasa Kumelj & Emil Karlsen & Eivind Almaas, 2021. "Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition," PLOS Computational Biology, Public Library of Science, vol. 17(5), pages 1-22, May.

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