Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network.

TitleSaccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network.
Publication TypeJournal Article
Year of Publication2003
AuthorsFamili I, Forster J, Nielsen J, Palsson BO
JournalProc Natl Acad Sci U S A
Volume100
Issue23
Pagination13134-9
Date Published2003 Nov 11
ISSN0027-8424
KeywordsAdenosine Triphosphate, Cell Division, Electron Transport, Gene Deletion, Genome, Fungal, Glucose, Models, Biological, Models, Genetic, Models, Theoretical, Phenotype, RNA, Messenger, Saccharomyces cerevisiae, Thermodynamics, Time Factors
Abstract

Full genome sequences of prokaryotic organisms have led to reconstruction of genome-scale metabolic networks and in silico computation of their integrated functions. The first genome-scale metabolic reconstruction for a eukaryotic cell, Saccharomyces cerevisiae, consisting of 1,175 metabolic reactions and 733 metabolites, has appeared. A constraint-based in silico analysis procedure was used to compute properties of the S. cerevisiae metabolic network. The computed number of ATP molecules produced per pair of electrons donated to the electron transport system (ETS) and energy-maintenance requirements were quantitatively in agreement with experimental results. Computed whole-cell functions of growth and metabolic by-product secretion in aerobic and anaerobic culture were consistent with experimental data, and thus mRNA expression profiles during metabolic shifts were computed. The computed consequences of gene knockouts on growth phenotypes were consistent with experimental observations. Thus, constraint-based analysis of a genome-scale metabolic network for the eukaryotic S. cerevisiae allows for computation of its integrated functions, producing in silico results that were consistent with observed phenotypic functions for approximately 70-80% of the conditions considered.

DOI10.1073/pnas.2235812100
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/14578455?dopt=Abstract
PMCPMC263729
Alternate TitleProc. Natl. Acad. Sci. U.S.A.
PubMed ID14578455