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

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 and Bernhard O. Palsson ()
Additional contact information
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.

Nature Communications, 2012, vol. 3, issue 1, 1-10

Abstract: 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.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.nature.com/articles/ncomms1928 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:3:y:2012:i:1:d:10.1038_ncomms1928

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

DOI: 10.1038/ncomms1928

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 ().