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BMC Bioinformatics. 2016 Sep 22;17(1):391.

solveME: fast and reliable solution of nonlinear ME models.

Author information

1
Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, USA.
2
Department of Management Science and Engineering, Stanford University, Stanford, 94305, CA, USA.
3
Department of Bioengineering, University of California at San Diego, La Jolla, 92093, CA, USA. palsson@ucsd.edu.
4
Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Building 220, Kongens Lyngby, DK-2800, Denmark. palsson@ucsd.edu.

Abstract

BACKGROUND:

Genome-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.

RESULTS:

Here, 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.

CONCLUSIONS:

Just 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.

KEYWORDS:

Constraint-based modeling; Metabolism; Nonlinear optimization; Proteome; Quasiconvex

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