Improving flux predictions by integrating data from multiple strains

Motivation: Incorporating experimental data into constraint‐based models can improve the quality and accuracy of their metabolic flux predictions. Unfortunately, routinely and easily measured experimental data such as growth rates, extracellular fluxes, transcriptomics and even proteomics are not always sufficient to significantly improve metabolic flux predictions. Results: We developed a new method (called REPPS) for incorporating experimental measurements of growth rates and extracellular fluxes from a set of perturbed reference strains (RSs) and a parental strain (PS) to substantially improve the predicted flux distribution of the parental strain. Using data from five single gene knockouts and the wild type strain, we decrease the mean squared error of predicted central metabolic fluxes by ˜47% compared to parsimonious flux balance analysis (pFBA). This decrease in error further improves flux predictions for new knockout strains. Furthermore, REPPS is less sensitive to the completeness of the metabolic network than pFBA. Availability and Implementation: Code is available in the Supplementary data available at Bioinformatics online. Contact: reed@engr.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

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

[2]  Daniel Machado,et al.  Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism , 2014, PLoS Comput. Biol..

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

[4]  G. Church,et al.  Analysis of optimality in natural and perturbed metabolic networks , 2002 .

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

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

[7]  I. Rocha,et al.  In Silico Constraint-Based Strain Optimization Methods: the Quest for Optimal Cell Factories , 2015, Microbiology and Molecular Reviews.

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

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

[10]  S. Lee,et al.  Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery , 2011, Molecular systems biology.

[11]  Chao Li,et al.  CeCaFDB: a curated database for the documentation, visualization and comparative analysis of central carbon metabolic flux distributions explored by 13C-fluxomics , 2014, Nucleic Acids Res..

[12]  Philip E. Bourne,et al.  Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model , 2010, PLoS Comput. Biol..

[13]  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.

[14]  M. Antoniewicz,et al.  COMPLETE-MFA: complementary parallel labeling experiments technique for metabolic flux analysis. , 2013, Metabolic engineering.

[15]  Stephen S Fong,et al.  Metabolic gene–deletion strains of Escherichia coli evolve to computationally predicted growth phenotypes , 2004, Nature Genetics.

[16]  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.

[17]  Christopher P. Long,et al.  Characterization of physiological responses to 22 gene knockouts in Escherichia coli central carbon metabolism. , 2016, Metabolic engineering.

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

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

[20]  Luiz E. Bermudez,et al.  Flux Balance Analysis with Objective Function Defined by Proteomics Data—Metabolism of Mycobacterium tuberculosis Exposed to Mefloquine , 2015, PloS one.

[21]  E. Ruppin,et al.  Regulatory on/off minimization of metabolic flux changes after genetic perturbations. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

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

[23]  E. Papoutsakis.  Equations and calculations for fermentations of butyric acid bacteria , 1984, Biotechnology and bioengineering.

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

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

[26]  Adam M. Feist,et al.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 , 2011, Molecular systems biology.

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