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Logical transformation of genome-scale metabolic models for gene level applications and analysis
East China University of Science and Technology, China; Chalmers University of Technology,Sweden. (Systems medicine)ORCID iD: 0000-0002-3721-8586
Number of Authors: 5
2015 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 31, no 14, 2324-2331 p.Article in journal (Refereed) Published
Abstract [en]

Motivation: In recent years, genome-scale metabolic models (GEMs) have played important roles in areas like systems biology and bioinformatics. However, because of the complexity of genereaction associations, GEMs often have limitations in gene level analysis and related applications. Hence, the existing methods were mainly focused on applications and analysis of reactions and metabolites. Results: Here, we propose a framework named logic transformation of model (LTM) that is able to simplify the gene-reaction associations and enables integration with other developed methods for gene level applications. We show that the transformed GEMs have increased reaction and metabolite number as well as degree of freedom in flux balance analysis, but the gene-reaction associations and the main features of flux distributions remain constant. In addition, we develop two methods, OptGeneKnock and FastGeneSL by combining LTM with previously developed reaction-based methods. We show that the FastGeneSL outperforms exhaustive search. Finally, we demonstrate the use of the developed methods in two different case studies. We could design fast genetic intervention strategies for targeted overproduction of biochemicals and identify double and triple synthetic lethal gene sets for inhibition of hepatocellular carcinoma tumor growth through the use of OptGeneKnock and FastGeneSL, respectively.

Place, publisher, year, edition, pages
Oxford University Press, 2015. Vol. 31, no 14, 2324-2331 p.
National Category
Biological Sciences Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:kth:diva-214107DOI: 10.1093/bioinformatics/btv134ISI: 000358173500012PubMedID: 25735769Scopus ID: 2-s2.0-84941656865OAI: oai:DiVA.org:kth-214107DiVA: diva2:1140286
Funder
Knut and Alice Wallenberg FoundationEU, European Research Council, 247013
Note

QC 20170918

Available from: 2017-09-11 Created: 2017-09-11 Last updated: 2017-09-18Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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