Identification of genome-scale metabolic network models using experimentally measured flux profiles.

TitleIdentification of genome-scale metabolic network models using experimentally measured flux profiles.
Publication TypeJournal Article
Year of Publication2006
AuthorsHerrgård MJ, Fong SS, Palsson BØ
JournalPLoS Comput Biol
Volume2
Issue7
Paginatione72
Date Published2006 Jul 7
ISSN1553-7358
KeywordsComputational Biology, Computer Simulation, Escherichia coli, Genetic Engineering, Genome, Bacterial, Models, Biological, Models, Genetic, Mutation, Phenotype, Systems Biology
Abstract

Genome-scale metabolic network models can be reconstructed for well-characterized organisms using genomic annotation and literature information. However, there are many instances in which model predictions of metabolic fluxes are not entirely consistent with experimental data, indicating that the reactions in the model do not match the active reactions in the in vivo system. We introduce a method for determining the active reactions in a genome-scale metabolic network based on a limited number of experimentally measured fluxes. This method, called optimal metabolic network identification (OMNI), allows efficient identification of the set of reactions that results in the best agreement between in silico predicted and experimentally measured flux distributions. We applied the method to intracellular flux data for evolved Escherichia coli mutant strains with lower than predicted growth rates in order to identify reactions that act as flux bottlenecks in these strains. The expression of the genes corresponding to these bottleneck reactions was often found to be downregulated in the evolved strains relative to the wild-type strain. We also demonstrate the ability of the OMNI method to diagnose problems in E. coli strains engineered for metabolite overproduction that have not reached their predicted production potential. The OMNI method applied to flux data for evolved strains can be used to provide insights into mechanisms that limit the ability of microbial strains to evolve towards their predicted optimal growth phenotypes. When applied to industrial production strains, the OMNI method can also be used to suggest metabolic engineering strategies to improve byproduct secretion. In addition to these applications, the method should prove to be useful in general for reconstructing metabolic networks of ill-characterized microbial organisms based on limited amounts of experimental data.

DOI10.1371/journal.pcbi.0020072
PubMed URLhttp://www.ncbi.nlm.nih.gov/pubmed/16839195?dopt=Abstract
PMCPMC1487183
Alternate JournalPLoS Comput. Biol.
PubMed ID16839195