solveME : fast and reliable solution of nonlinear ME models

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

30 Scopus Citations
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  • Laurence Yang
  • Ali Ebrahim
  • Colton J. Lloyd
  • Michael A. Saunders
  • Bernhard O. Palsson


Original languageEnglish
Article number391
Number of pages10
Journal / PublicationBMC Bioinformatics
Online published22 Sept 2016
Publication statusPublished - 2016
Externally publishedYes



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.

Research Area(s)

  • Nonlinear optimization, Constraint-based modeling, Metabolism, Proteome, Quasiconvex

Citation Format(s)

solveME: fast and reliable solution of nonlinear ME models. / Yang, Laurence; Ma, Ding; Ebrahim, Ali et al.
In: BMC Bioinformatics, Vol. 17, 391, 2016.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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