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

solveME: fast and reliable solution of nonlinear ME models.

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



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.


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.


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.


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

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