solveME: fast and reliable solution of nonlinear ME models

BMC Bioinformatics

Methodology Article

Ali Ebrahim¹  Laurence Yang¹  Colton J. Lloyd¹  Bernhard O. Palsson²  Ding Ma³  Michael A. Saunders³ 

[1] Department of Bioengineering, University of California at San Diego, 92093, La Jolla, CA, USA;Department of Bioengineering, University of California at San Diego, 92093, La Jolla, CA, USA;Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, DK-2800, Kongens Lyngby, Denmark;Department of Management Science and Engineering, Stanford University, 94305, Stanford, CA, USA;

关键词: Nonlinear optimization;    Constraint-based modeling;    Metabolism;    Proteome;    Quasiconvex;   

DOI  :  10.1186/s12859-016-1240-1

 received in 2016-03-16, accepted in 2016-09-06,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2016

【 图 表 】

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