solveME: fast and reliable solution of nonlinear ME models

BackgroundGenome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. ME models present considerable computational challenges: they are much (>30 times) larger than corresponding metabolic reconstructions (M models), are multiscale, and growth maximization is a nonlinear programming (NLP) problem, mainly due to macromolecule dilution constraints.ResultsHere, we address these computational challenges. We develop a fast and numerically reliable solution method for growth maximization in ME models using a quad-precision NLP solver (Quad MINOS). Our method was up to 45 % faster than binary search for six significant digits in growth rate. We also develop a fast, quad-precision flux variability analysis that is accelerated (up to 60× speedup) via solver warm-starts. Finally, we employ the tools developed to investigate growth-coupled succinate overproduction, accounting for proteome constraints.ConclusionsJust as genome-scale metabolic reconstructions have become an invaluable tool for computational and systems biologists, we anticipate that these fast and numerically reliable ME solution methods will accelerate the wide-spread adoption of ME models for researchers in these fields.

[1]  Ka-Yiu San,et al.  Batch culture characterization and metabolic flux analysis of succinate-producing Escherichia coli strains. , 2006, Metabolic engineering.

[2]  Paul Tseng,et al.  Gilding the Lily: A Variant of the Nelder-Mead Algorithm Based on Golden-Section Search , 2002, Comput. Optim. Appl..

[3]  Edward J. O'Brien,et al.  Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data , 2015, Proceedings of the National Academy of Sciences.

[4]  Kaspar Valgepea,et al.  Lean-Proteome Strains – Next Step in Metabolic Engineering , 2015, Front. Bioeng. Biotechnol..

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

[6]  Steffen Klamt,et al.  An algorithm for the reduction of genome-scale metabolic network models to meaningful core models , 2015, BMC Systems Biology.

[7]  Edward J. O'Brien,et al.  Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale , 2014, BMC Systems Biology.

[8]  B. Palsson,et al.  Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods , 2012, Nature Reviews Microbiology.

[9]  S. M. Robinson,et al.  A quadratically-convergent algorithm for general nonlinear programming problems , 1972, Math. Program..

[10]  Edward J. O'Brien,et al.  Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments , 2016, PLoS Comput. Biol..

[11]  Ronan M. T. Fleming,et al.  Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression , 2016, Scientific Reports.

[12]  Joshua A. Lerman,et al.  COBRApy: COnstraints-Based Reconstruction and Analysis for Python , 2013, BMC Systems Biology.

[13]  Michael A. Saunders,et al.  A projected Lagrangian algorithm and its implementation for sparse nonlinear constraints , 1982 .

[14]  Michael A. Saunders,et al.  Robust flux balance analysis of multiscale biochemical reaction networks , 2013, BMC Bioinformatics.

[15]  Edward J. O'Brien,et al.  Computing the functional proteome: recent progress and future prospects for genome-scale models. , 2015, Current opinion in biotechnology.

[16]  Joshua A. Lerman,et al.  Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments , 2013, Proceedings of the National Academy of Sciences.

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

[18]  Karsten Zengler,et al.  Engineering of oleaginous organisms for lipid production. , 2015, Current opinion in biotechnology.

[19]  William R Cluett,et al.  EMILiO: a fast algorithm for genome-scale strain design. , 2011, Metabolic engineering.

[20]  Ronan M. T. Fleming,et al.  Multiscale Modeling of Metabolism and Macromolecular Synthesis in E. coli and Its Application to the Evolution of Codon Usage , 2012, PloS one.

[21]  Bernhard O Palsson,et al.  Predicting microbial growth , 2014, Science.

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

[23]  Roland Wunderling,et al.  Paralleler und objektorientierter Simplex-Algorithmus , 1996 .

[24]  Jon C. Dattorro,et al.  Convex Optimization & Euclidean Distance Geometry , 2004 .

[25]  Markus J. Herrgård,et al.  Multi-scale exploration of the technical, economic, and environmental dimensions of bio-based chemical production. , 2015, Metabolic engineering.

[26]  Jeffrey D. Orth,et al.  In silico method for modelling metabolism and gene product expression at genome scale , 2012, Nature Communications.

[27]  S. Klamt,et al.  On the feasibility of growth-coupled product synthesis in microbial strains. , 2015, Metabolic engineering.

[28]  Pearu Peterson,et al.  F2PY: a tool for connecting Fortran and Python programs , 2009, Int. J. Comput. Sci. Eng..

[29]  M. Saunders,et al.  Solving Multiscale Linear Programs Using the Simplex Method in Quadruple Precision , 2015 .

[30]  Adam M. Feist,et al.  Next-generation genome-scale models for metabolic engineering. , 2015, Current opinion in biotechnology.

[31]  W. R. Cluett,et al.  Characterizing metabolic pathway diversification in the context of perturbation size. , 2015, Metabolic engineering.

[32]  Steffen Klamt,et al.  Genome-scale strain designs based on regulatory minimal cut sets , 2015, Bioinform..

[33]  Ines Thiele,et al.  Computationally efficient flux variability analysis , 2010, BMC Bioinformatics.

[34]  Edward J. O'Brien,et al.  Using Genome-scale Models to Predict Biological Capabilities , 2015, Cell.

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