Kuepfer2005 - Genome-scale metabolic network of Saccharomyces cerevisiae (iLL672)
Ontology highlight
ABSTRACT:
Kuepfer2005 - Genome-scale metabolic network
of Saccharomyces cerevisiae (iLL672)
This model is described in the article:
Metabolic functions of
duplicate genes in Saccharomyces cerevisiae.
Kuepfer L, Sauer U, Blank LM.
Genome Res. 2005 Oct; 15(10):
1421-1430
Abstract:
The roles of duplicate genes and their contribution to the
phenomenon of enzyme dispensability are a central issue in
molecular and genome evolution. A comprehensive classification
of the mechanisms that may have led to their preservation,
however, is currently lacking. In a systems biology approach,
we classify here back-up, regulatory, and gene dosage functions
for the 105 duplicate gene families of Saccharomyces cerevisiae
metabolism. The key tool was the reconciled genome-scale
metabolic model iLL672, which was based on the older iFF708.
Computational predictions of all metabolic gene knockouts were
validated with the experimentally determined phenotypes of the
entire singleton yeast library of 4658 mutants under five
environmental conditions. iLL672 correctly identified 96%-98%
and 73%-80% of the viable and lethal singleton phenotypes,
respectively. Functional roles for each duplicate family were
identified by integrating the iLL672-predicted in silico
duplicate knockout phenotypes, genome-scale carbon-flux
distributions, singleton mutant phenotypes, and network
topology analysis. The results provide no evidence for a
particular dominant function that maintains duplicate genes in
the genome. In particular, the back-up function is not favored
by evolutionary selection because duplicates do not occur more
frequently in essential reactions than singleton genes. Instead
of a prevailing role, multigene-encoded enzymes cover different
functions. Thus, at least for metabolism, persistence of the
paralog fraction in the genome can be better explained with an
array of different, often overlapping functional roles.
This model is hosted on
BioModels Database
and identified by:
MODEL1507180066.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
SUBMITTER: 
Nicolas Le Novère
PROVIDER: MODEL1507180066 | BioModels | 2015-07-30
REPOSITORIES: BioModels
ACCESS DATA
