solveME: Fast and reliable solution of nonlinear ME models

Laurence Yang, Ding Ma, Ali Ebrahim, Colton J. Lloyd, Michael A. Saunders, Bernhard O. Palsson*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

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.

Original languageEnglish
Article number391
JournalBMC Bioinformatics
Volume17
Issue number1
DOIs
Publication statusPublished - 22 Sept 2016

Bibliographical note

Publisher Copyright:
© 2016 The Author(s).

Other keywords

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

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