Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism

Constraint-based models of metabolism are a widely used framework for predicting flux distributions in genome-scale biochemical networks. The number of published methods for integration of transcriptomic data into constraint-based models has been rapidly increasing. So far the predictive capability of these methods has not been critically evaluated and compared. This work presents a survey of recently published methods that use transcript levels to try to improve metabolic flux predictions either by generating flux distributions or by creating context-specific models. A subset of these methods is then systematically evaluated using published data from three different case studies in E. coli and S. cerevisiae. The flux predictions made by different methods using transcriptomic data are compared against experimentally determined extracellular and intracellular fluxes (from 13C-labeling data). The sensitivity of the results to method-specific parameters is also evaluated, as well as their robustness to noise in the data. The results show that none of the methods outperforms the others for all cases. Also, it is observed that for many conditions, the predictions obtained by simple flux balance analysis using growth maximization and parsimony criteria are as good or better than those obtained using methods that incorporate transcriptomic data. We further discuss the differences in the mathematical formulation of the methods, and their relation to the results we have obtained, as well as the connection to the underlying biological principles of metabolic regulation.

[1]  U. Sauer,et al.  Multidimensional Optimality of Microbial Metabolism , 2012, Science.

[2]  Jason A. Papin,et al.  Functional integration of a metabolic network model and expression data without arbitrary thresholding , 2011, Bioinform..

[3]  Jonathan R. Karr,et al.  A Whole-Cell Computational Model Predicts Phenotype from Genotype , 2012, Cell.

[4]  Merja Penttilä,et al.  Low oxygen levels as a trigger for enhancement of respiratory metabolism in Saccharomyces cerevisiae , 2009, BMC Genomics.

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

[6]  U. Sauer,et al.  Somewhat in control--the role of transcription in regulating microbial metabolic fluxes. , 2013, Current opinion in biotechnology.

[7]  Intawat Nookaew,et al.  Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling , 2013, BMC Systems Biology.

[8]  C. Gleissner.  Macrophage Phenotype Modulation by CXCL4 in Atherosclerosis , 2011, Front. Physio..

[9]  Eytan Ruppin,et al.  iMAT: an integrative metabolic analysis tool , 2010, Bioinform..

[10]  Miguel Rocha,et al.  Modeling formalisms in Systems Biology , 2011, AMB Express.

[11]  Bernhard O. Palsson,et al.  GIM3E: condition-specific models of cellular metabolism developed from metabolomics and expression data , 2013, Bioinform..

[12]  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.

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

[14]  Hyun Uk Kim,et al.  Flux-coupled genes and their use in metabolic flux analysis. , 2013, Biotechnology journal.

[15]  Erez Lieberman Aiden,et al.  The expanding scope of DNA sequencing , 2012, Nature Biotechnology.

[16]  S. Schuster,et al.  ON ELEMENTARY FLUX MODES IN BIOCHEMICAL REACTION SYSTEMS AT STEADY STATE , 1994 .

[17]  J. Pronk,et al.  Role of Transcriptional Regulation in Controlling Fluxes in Central Carbon Metabolism of Saccharomyces cerevisiae , 2004, Journal of Biological Chemistry.

[18]  L. Blank,et al.  Metabolic and Transcriptional Response to Cofactor Perturbations in Escherichia coli , 2010, The Journal of Biological Chemistry.

[19]  Nathan E Lewis,et al.  Analysis of omics data with genome-scale models of metabolism. , 2013, Molecular bioSystems.

[20]  F. Doyle,et al.  Dynamic flux balance analysis of diauxic growth in Escherichia coli. , 2002, Biophysical journal.

[21]  Marcel J. T. Reinders,et al.  Predicting Metabolic Fluxes Using Gene Expression Differences As Constraints , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[22]  Jason A. Papin,et al.  Integration of expression data in genome-scale metabolic network reconstructions , 2012, Front. Physio..

[23]  A. Hoppe.  What mRNA Abundances Can Tell us about Metabolism , 2012, Metabolites.

[24]  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.

[25]  Ronan M. T. Fleming,et al.  Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0 , 2007, Nature Protocols.

[26]  Bernhard O. Palsson,et al.  Context-Specific Metabolic Networks Are Consistent with Experiments , 2008, PLoS Comput. Biol..

[27]  Nathan D. Price,et al.  Reconstruction of genome-scale metabolic models for 126 human tissues using mCADRE , 2012, BMC Systems Biology.

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

[29]  Barbara M. Bakker,et al.  The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels , 2007, Proceedings of the National Academy of Sciences.

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

[31]  Jaques Reifman,et al.  Modeling Phenotypic Metabolic Adaptations of Mycobacterium tuberculosis H37Rv under Hypoxia , 2012, PLoS Comput. Biol..

[32]  Zoran Nikoloski,et al.  Integration of time-resolved transcriptomics data with flux-based methods reveals stress-induced metabolic adaptation in Escherichia coli , 2012, BMC Systems Biology.

[33]  E. Ruppin,et al.  Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism , 2010, Molecular systems biology.

[34]  Jason A. Papin,et al.  TIGER: Toolbox for integrating genome-scale metabolic models, expression data, and transcriptional regulatory networks , 2011, BMC Systems Biology.

[35]  B. Palsson,et al.  Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth , 2002, Nature.

[36]  Jennifer L. Reed,et al.  Mechanistic analysis of multi-omics datasets to generate kinetic parameters for constraint-based metabolic models , 2013, BMC Bioinformatics.

[37]  Edward J. O'Brien,et al.  Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction , 2013, Molecular systems biology.

[38]  J. Reed,et al.  RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations , 2012, Genome Biology.

[39]  Pei Yee Ho,et al.  Multiple High-Throughput Analyses Monitor the Response of E. coli to Perturbations , 2007, Science.

[40]  E. Muller,et al.  The dynamics of the macromolecular composition of biomass. , 2001, Journal of theoretical biology.

[41]  Madhukar S. Dasika,et al.  Metabolic flux elucidation for large-scale models using 13C labeled isotopes. , 2007, Metabolic engineering.

[42]  S. Lee,et al.  Strategies for systems‐level metabolic engineering , 2008, Biotechnology journal.

[43]  Markus J. Herrgård,et al.  Network-based prediction of human tissue-specific metabolism , 2008, Nature Biotechnology.

[44]  Hans Lehrach,et al.  Metabolic reconfiguration precedes transcriptional regulation in the antioxidant response , 2009, Nature Biotechnology.

[45]  Martijn A. Huynen,et al.  Inferring Metabolic States in Uncharacterized Environments Using Gene-Expression Measurements , 2013, PLoS Comput. Biol..

[46]  J. Nielsen,et al.  Integration of gene expression data into genome-scale metabolic models. , 2004, Metabolic engineering.

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

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

[49]  Jens Nielsen,et al.  Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes , 2010, PLoS Comput. Biol..

[50]  Adam M. Feist,et al.  Basic and applied uses of genome-scale metabolic network reconstructions of Escherichia coli , 2013, Molecular systems biology.

[51]  Michael C. Jewett,et al.  Linking high-resolution metabolic flux phenotypes and transcriptional regulation in yeast modulated by the global regulator Gcn4p , 2009, Proceedings of the National Academy of Sciences.

[52]  Natapol Pornputtapong,et al.  Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT , 2012, PLoS Comput. Biol..

[53]  Sara B. Collins,et al.  Temporal Expression-based Analysis of Metabolism , 2012, PLoS Comput. Biol..

[54]  Dan Ventura,et al.  Novel algorithms and the benefits of comparative validation , 2013, Bioinform..

[55]  Anton Nekrutenko,et al.  Ten Simple Rules for Reproducible Computational Research , 2013, PLoS Comput. Biol..

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

[57]  N. Price,et al.  Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis , 2010, Proceedings of the National Academy of Sciences.

[58]  Jason A. Papin,et al.  Applications of genome-scale metabolic reconstructions , 2009, Molecular systems biology.

[59]  J. Harrow,et al.  Systematic evaluation of spliced alignment programs for RNA-seq data , 2013, Nature Methods.

[60]  Tomer Shlomi,et al.  Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters , 2012, PLoS Comput. Biol..

[61]  J. Galagan,et al.  Inferring Carbon Sources from Gene Expression Profiles Using Metabolic Flux Models , 2012, PloS one.

[62]  J. Harrow,et al.  Assessment of transcript reconstruction methods for RNA-seq , 2013, Nature Methods.

[63]  Eytan Ruppin,et al.  Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model , 2010, Bioinform..

[64]  Jennifer L. Reed,et al.  Shrinking the Metabolic Solution Space Using Experimental Datasets , 2012, PLoS Comput. Biol..