The role of flexibility and optimality in the prediction of intracellular fluxes of microbial central carbon metabolism.

Prediction of intracellular metabolic fluxes based on optimal biomass assumption is a well-known computational approach. While there has been a significant emphasis on the optimality, cellular flexibility, the co-occurrence of suboptimal flux distributions in a microbial population, has hardly been considered in the related computational methods. We have implemented a flexibility-incorporated optimization framework to calculate intracellular fluxes based on a few extracellular measurement constraints. Taking into account slightly suboptimal flux distributions together with a dual-optimality framework (maximization of the growth rate followed by the minimization of the total enzyme amount) we were able to show the positive effect of incorporating flexibility and minimal enzyme consumption on the better prediction of intracellular fluxes of central carbon metabolism of two microorganisms: E. coli and S. cerevisiae.

[1]  R. Mahadevan,et al.  The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. , 2003, Metabolic engineering.

[2]  Desmond S. Lun,et al.  Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production , 2009, PLoS Comput. Biol..

[3]  B. Palsson,et al.  Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110 , 1994, Applied and environmental microbiology.

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

[5]  K. Ülgen,et al.  Reconstruction and flux analysis of coupling between metabolic pathways of astrocytes and neurons: application to cerebral hypoxia , 2007, Theoretical Biology and Medical Modelling.

[6]  Tom M. Conrad,et al.  Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models , 2010, Molecular systems biology.

[7]  K. Ülgen,et al.  Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks , 2004, Biotechnology and bioengineering.

[8]  Jens Nielsen,et al.  Effect of carbon source perturbations on transcriptional regulation of metabolic fluxes in Saccharomyces cerevisiae , 2007, BMC Systems Biology.

[9]  U. Alon,et al.  Just-in-time transcription program in metabolic pathways , 2004, Nature Genetics.

[10]  U. Sauer,et al.  Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli , 2007, Molecular systems biology.

[11]  U. Sauer,et al.  Metabolic Flux Responses to Pyruvate Kinase Knockout in Escherichia coli , 2002, Journal of bacteriology.

[12]  Lars M. Blank,et al.  Correlation between TCA cycle flux and glucose uptake rate during respiro-fermentative growth of Saccharomyces cerevisiae. , 2009, Microbiology.

[13]  Jochen Förster,et al.  Modeling Lactococcus lactis using a genome-scale flux model , 2005, BMC Microbiology.

[14]  P. Mendes,et al.  The origin of correlations in metabolomics data , 2005, Metabolomics.

[15]  B. Palsson,et al.  Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[16]  A. Burgard,et al.  Optimization-based framework for inferring and testing hypothesized metabolic objective functions. , 2003, Biotechnology and bioengineering.

[17]  Jeffrey D Orth,et al.  What is flux balance analysis? , 2010, Nature Biotechnology.

[18]  Eytan Ruppin,et al.  Conservation of Expression and Sequence of Metabolic Genes Is Reflected by Activity Across Metabolic States , 2006, PLoS Comput. Biol..

[19]  Wei Sha,et al.  A Systems Biology Study of Two Distinct Growth Phases of Saccharomyces cerevisiae Cultures , 2004 .

[20]  Evangelos Simeonidis,et al.  Flux balance analysis: a geometric perspective. , 2009, Journal of theoretical biology.

[21]  J Tramper,et al.  Metabolic flux analysis of hybridoma cells in different culture media using mass balances , 1996, Biotechnology and bioengineering.

[22]  U. Sauer,et al.  Escherichia coli† , 2004 .

[23]  J. Keasling,et al.  Stoichiometric model of Escherichia coli metabolism: incorporation of growth-rate dependent biomass composition and mechanistic energy requirements. , 1997, Biotechnology and bioengineering.

[24]  Jens Nielsen,et al.  Reconstruction of the central carbon metabolism of Aspergillus niger. , 2003, European journal of biochemistry.

[25]  Age K. Smilde,et al.  Metabolic network discovery through reverse engineering of metabolome data , 2009, Metabolomics.

[26]  B. Palsson,et al.  Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network. , 2003, Genome research.

[27]  B. Palsson,et al.  Genome-scale Reconstruction of Metabolic Network in Bacillus subtilis Based on High-throughput Phenotyping and Gene Essentiality Data* , 2007, Journal of Biological Chemistry.

[28]  J. Pronk,et al.  When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation , 2006, Molecular systems biology.

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

[30]  H. Holzhütter The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. , 2004, European journal of biochemistry.

[31]  Kenneth J. Kauffman,et al.  Advances in flux balance analysis. , 2003, Current opinion in biotechnology.

[32]  Uwe Sauer,et al.  Transcriptional regulation of respiration in yeast metabolizing differently repressive carbon substrates , 2010, BMC Systems Biology.

[33]  Mehmet A. Orman,et al.  Pathway analysis of liver metabolism under stressed condition. , 2011, Journal of theoretical biology.

[34]  U. Sauer,et al.  A Novel Metabolic Cycle Catalyzes Glucose Oxidation and Anaplerosis in Hungry Escherichia coli* , 2003, Journal of Biological Chemistry.

[35]  Rishi Jain,et al.  Bayesian-based selection of metabolic objective functions , 2007 .

[36]  J E Bailey,et al.  Metabolic capacity of Bacillus subtilis for the production of purine nucleosides, riboflavin, and folic acid. , 1998, Biotechnology and bioengineering.

[37]  Annik Nanchen,et al.  Nonlinear Dependency of Intracellular Fluxes on Growth Rate in Miniaturized Continuous Cultures of Escherichia coli , 2006, Applied and Environmental Microbiology.

[38]  H. Y. Steensma,et al.  Effects of Pyruvate Decarboxylase Overproduction on Flux Distribution at the Pyruvate Branch Point inSaccharomyces cerevisiae , 1998, Applied and Environmental Microbiology.

[39]  J. Pronk,et al.  Reproducibility of Oligonucleotide Microarray Transcriptome Analyses , 2002, The Journal of Biological Chemistry.

[40]  J. Nielsen,et al.  Network Identification and Flux Quantification in the Central Metabolism of Saccharomyces cerevisiae under Different Conditions of Glucose Repression , 2001, Journal of bacteriology.

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

[42]  Ali Navid,et al.  Genome-level transcription data of Yersinia pestis analyzed with a New metabolic constraint-based approach , 2012, BMC Systems Biology.

[43]  U. Sauer,et al.  Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast , 2005, Genome Biology.

[44]  S. Schuster,et al.  Metabolic network structure determines key aspects of functionality and regulation , 2002, Nature.

[45]  Jens Nielsen,et al.  Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains. , 2010, Nature communications.

[46]  Merja Penttilä,et al.  Oxygen dependence of metabolic fluxes and energy generation of Saccharomyces cerevisiae CEN.PK113-1A , 2008, BMC Systems Biology.

[47]  Z. Soons,et al.  Identification of Metabolic Engineering Targets through Analysis of Optimal and Sub-Optimal Routes , 2013, PloS one.

[48]  L. Blank,et al.  Metabolic capacity estimation of Escherichia coli as a platform for redox biocatalysis: constraint‐based modeling and experimental verification , 2008, Biotechnology and bioengineering.

[49]  Neil Swainston,et al.  Improving metabolic flux predictions using absolute gene expression data , 2012, BMC Systems Biology.

[50]  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.