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PMID: 19282964 Published · ppublish English Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S. Validation Study

GrowMatch: an automated method for reconciling in silico/in vivo growth predictions.

PLoS computational biology ·Vol. 5 ·No. 3 ·2009-03-00 ·Pages e1000308

Kumar VS, Maranas CD

Abstract

Genome-scale metabolic reconstructions are typically validated by comparing in silico growth predictions across different mutants utilizing different carbon sources with in vivo growth data. This comparison results in two types of model-prediction inconsistencies; either the model predicts growth when no growth is observed in the experiment (GNG inconsistencies) or the model predicts no growth when the experiment reveals growth (NGG inconsistencies). Here we propose an optimization-based framework, GrowMatch, to automatically reconcile GNG predictions (by suppressing functionalities in the model) and NGG predictions (by adding functionalities to the model). We use GrowMatch to resolve inconsistencies between the predictions of the latest in silico Escherichia coli (iAF1260) model and the in vivo data available in the Keio collection and improved the consistency of in silico with in vivo predictions from 90.6% to 96.7%. Specifically, we were able to suggest consistency-restoring hypotheses for 56/72 GNG mutants and 13/38 NGG mutants. GrowMatch resolved 18 GNG inconsistencies by suggesting suppressions in the mutant metabolic networks. Fifteen inconsistencies were resolved by suppressing isozymes in the metabolic network, and the remaining 23 GNG mutants corresponding to blocked genes were resolved by suitably modifying the biomass equation of iAF1260. GrowMatch suggested consistency-restoring hypotheses for five NGG mutants by adding functionalities to the model whereas the remaining eight inconsistencies were resolved by pinpointing possible alternate genes that carry out the function of the deleted gene. For many cases, GrowMatch identified fairly nonintuitive model modification hypotheses that would have been difficult to pinpoint through inspection alone. In addition, GrowMatch can be used during the construction phase of new, as opposed to existing, genome-scale metabolic models, leading to more expedient and accurate reconstructions.

MeSH Terms
Algorithms Cell Proliferation Computer Simulation Escherichia coli/growth & development,metabolism Escherichia coli Proteins/physiology Gene Expression Regulation, Bacterial/physiology Models, Biological Signal Transduction/physiology
Chemicals
Escherichia coli Proteins
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Kumar Vinay Satish
Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, Pennsylvania, United States of America.
Maranas Costas D
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Article Info
Journal
PLoS computational biology
Abbr.
PLoS Comput Biol
ISSN
1553-7358
DOI
10.1371/journal.pcbi.1000308
Published
2009-03-00
Epub
2009-00-13
Pages
e1000308
Language
English
Region
United States
NLM ID
101238922
PMCID
PMC2645679
Subset
IM
External Links
PubMed source DOI: 10.1371/journal.pcbi.1000308 PMC Full Text View journal details
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