solveME: fast and reliable solution of nonlinear ME models.

TitlesolveME: fast and reliable solution of nonlinear ME models.
Publication TypeJournal Article
Year of Publication2016
AuthorsYang L, Ma D, Ebrahim A, Lloyd CJ, Saunders MA, Palsson BO
JournalBMC Bioinformatics
Volume17
Issue1
Pagination391
PubMed Date09/2016
ISSN1471-2105
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.

Alternate JournalBMC Bioinformatics
PubMed ID27659412
Cover Image: 

Location

Location

417 Powell-Focht Bioengineering Hall

9500 Gilman Drive La Jolla, CA 92093-0412

Contact Us

Contact Us

In Silico Lab:  858-822-1144

Wet Lab:  858-246-1625

FAX:   858-822-3120

Website Concerns: sbrgit@ucsd.edu

User Login