Literature DB >> 14506848

Large-scale evaluation of in silico gene deletions in Saccharomyces cerevisiae.

Jochen Förster1, Iman Famili, Bernhard O Palsson, Jens Nielsen.   

Abstract

A large-scale in silico evaluation of gene deletions in Saccharomyces cerevisiae was conducted using a genome-scale reconstructed metabolic model. The effect of 599 single gene deletions on cell viability was simulated in silico and compared to published experimental results. In 526 cases (87.8%), the in silico results were in agreement with experimental observations when growth on synthetic complete medium was simulated. Viable phenotypes were correctly predicted in 89.4% (496 out of 555) and lethal phenotypes were correctly predicted in 68.2% (30 out of 44) of the cases considered. The in silico evaluation was solely based on the topological properties of the metabolic network which is based on well-established reaction stoichiometry. No interaction or regulatory information was accounted for in the in silico model. False predictions were analyzed on a case-by-case basis for four possible inadequacies of the in silico model: (1) incomplete media composition, (2) substitutable biomass components, (3) incomplete biochemical information, and (4) missing regulation. This analysis eliminated a number of false predictions and suggested a number of experimentally testable hypotheses. A genome-scale in silico model can thus be used to systematically reconcile existing data and fill in our knowledge gaps about an organism.

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Year:  2003        PMID: 14506848     DOI: 10.1089/153623103322246584

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  53 in total

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2.  Superessential reactions in metabolic networks.

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Journal:  Proc Natl Acad Sci U S A       Date:  2012-04-16       Impact factor: 11.205

Review 3.  A network perspective on metabolism and aging.

Authors:  Quinlyn A Soltow; Dean P Jones; Daniel E L Promislow
Journal:  Integr Comp Biol       Date:  2010-07-12       Impact factor: 3.326

4.  Regulatory on/off minimization of metabolic flux changes after genetic perturbations.

Authors:  Tomer Shlomi; Omer Berkman; Eytan Ruppin
Journal:  Proc Natl Acad Sci U S A       Date:  2005-05-16       Impact factor: 11.205

Review 5.  Metabolic engineering in the -omics era: elucidating and modulating regulatory networks.

Authors:  Goutham N Vemuri; Aristos A Aristidou
Journal:  Microbiol Mol Biol Rev       Date:  2005-06       Impact factor: 11.056

6.  Candidate states of Helicobacter pylori's genome-scale metabolic network upon application of "loop law" thermodynamic constraints.

Authors:  Nathan D Price; Ines Thiele; Bernhard Ø Palsson
Journal:  Biophys J       Date:  2006-03-13       Impact factor: 4.033

7.  Predicting growth rate from gene expression.

Authors:  Thomas P Wytock; Adilson E Motter
Journal:  Proc Natl Acad Sci U S A       Date:  2018-12-21       Impact factor: 11.205

8.  Systems-level engineering of nonfermentative metabolism in yeast.

Authors:  Caleb J Kennedy; Patrick M Boyle; Zeev Waks; Pamela A Silver
Journal:  Genetics       Date:  2009-06-29       Impact factor: 4.562

9.  Metabolic functions of duplicate genes in Saccharomyces cerevisiae.

Authors:  Lars Kuepfer; Uwe Sauer; Lars M Blank
Journal:  Genome Res       Date:  2005-10       Impact factor: 9.043

10.  Flux balance analysis of barley seeds: a computational approach to study systemic properties of central metabolism.

Authors:  Eva Grafahrend-Belau; Falk Schreiber; Dirk Koschützki; Björn H Junker
Journal:  Plant Physiol       Date:  2008-11-05       Impact factor: 8.340

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