Systematizing the generation of missing metabolic knowledge

Genome-scale metabolic network reconstructions are built from all of the known metabolic reactions and genes in a target organism. However, since our knowledge of any organism is incomplete, these network reconstructions contain gaps. Reactions may be missing, resulting in dead-ends in pathways, whi...

Full description

Saved in:
Bibliographic Details
Published in:Biotechnology and bioengineering 2010-10, Vol.107 (3), p.403-412
Main Authors: Orth, Jeffrey D, Palsson, Bernhard Ø
Format: Article
Language:English
Subjects:
Biological and medical sciences
Biomedical Research - methods
Biotechnology
Computation
Computational mathematics
Fundamental and applied biological sciences. Psychology
gap-filling
Gaps
gene annotation
General aspects
Genes
Genetics
Genomics
Genotype & phenotype
growmatch
Mathematical models
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
metabolic network reconstruction
Metabolic Networks and Pathways
Metabolism
Metabolome
Networks
Organisms
Reconstruction
SMILEY
Systems Biology
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Genome-scale metabolic network reconstructions are built from all of the known metabolic reactions and genes in a target organism. However, since our knowledge of any organism is incomplete, these network reconstructions contain gaps. Reactions may be missing, resulting in dead-ends in pathways, while unknown gene products may catalyze known reactions. New computational methods that analyze data, such as growth phenotypes or gene essentiality, in the context of genome-scale metabolic networks, have been developed to predict these missing reactions or genes likely to fill these knowledge gaps. A growing number of experimental studies are appearing that address these computational predictions, leading to discovery of new metabolic capabilities in the target organism. Gap-filling methods can thus be used to improve metabolic network models while simultaneously leading to discovery of new metabolic gene functions. Biotechnol. Bioeng. 2010;107: 403-412.
ISSN:0006-3592
1097-0290
1097-0290
DOI:10.1002/bit.22844