Analysis of the Effect of Reversibility Constraints on the Predictions of Genome-Scale Metabolic Models

Reversibility constraints are one aspect of genome-scale metabolic models that has received significant attention recently. This study explores the impact of complete removal of reversibility constraints on the gene essentiality and growth phenotype predictions generated using three published genome-scale metabolic models: the iJR904, the iAF1260, and the iBsu1103. In all three models, the accuracy in predicting essential genes declined significantly with the relaxation of reversibility constraints, while the accuracy in predicting nonessential genes increased only for the iJR904 and iAF1260 model. Additionally, the number of inactive reactions in all models declined substantially with the relaxation of the reversibility constraints. This study rapidly reveals the extent to which the reversibility constraints included in a metabolic model have been optimized, and it indicates those incorrect model predictions that may be repaired and those correct model predictions that may be broken by increasing the number of reversible reactions in a model.

[1]  B. Palsson,et al.  Metabolic capabilities of Escherichia coli: I. synthesis of biosynthetic precursors and cofactors. , 1993, Journal of theoretical biology.

[2]  Adam M. Feist,et al.  A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information , 2007, Molecular systems biology.

[3]  Andrew R. Joyce,et al.  Experimental and Computational Assessment of Conditionally Essential Genes in Escherichia coli , 2006, Journal of bacteriology.

[4]  Rick L Stevens,et al.  iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations , 2009, Genome Biology.

[5]  E. Papoutsakis,et al.  Equations and calculations of product yields and preferred pathways for butanediol and mixed‐acid fermentations , 1985, Biotechnology and bioengineering.

[6]  Q. Ouyang,et al.  Expanded flux variability analysis on metabolic network of Escherichia coli , 2009 .

[7]  B. Bochner.  Global phenotypic characterization of bacteria , 2008, FEMS microbiology reviews.

[8]  Vinay Satish Kumar,et al.  GrowMatch: An Automated Method for Reconciling In Silico/In Vivo Growth Predictions , 2009, PLoS Comput. Biol..

[9]  Adam M. Feist,et al.  Reconstruction of biochemical networks in microorganisms , 2009, Nature Reviews Microbiology.

[10]  H. Mori,et al.  Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection , 2006, Molecular systems biology.

[11]  Fangfang Xia,et al.  Application of high-performance computing to the reconstruction, analysis, and optimization of genome-scale metabolic models , 2009 .

[12]  Matthias Heinemann,et al.  Systematic assignment of thermodynamic constraints in metabolic network models , 2006, BMC Bioinformatics.

[13]  B. Palsson,et al.  An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR) , 2003, Genome Biology.

[14]  B. Palsson,et al.  The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[15]  S. Ehrlich,et al.  Essential Bacillus subtilis genes , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Matthew D. Jankowski,et al.  Genome-scale thermodynamic analysis of Escherichia coli metabolism. , 2006, Biophysical journal.

[17]  B. Palsson,et al.  Metabolic Capabilities of Escherichia coli II. Optimal Growth Patterns , 1993 .

[18]  Vinay Satish Kumar,et al.  Optimization based automated curation of metabolic reconstructions , 2007, BMC Bioinformatics.

[19]  Adam M. Feist,et al.  The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli , 2008, Nature Biotechnology.

[20]  B. Palsson,et al.  In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data , 2001, Nature Biotechnology.