Paper Info
Title
solveME: fast and reliable solution of nonlinear ME models.
Abstract
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.
Year
DOI
Venue
2016
10.1186/s12859-016-1240-1
BMC Bioinformatics
Keywords
Field
DocType
Constraint-based modeling,Metabolism,Nonlinear optimization,Proteome,Quasiconvex
Nonlinear system,Computer science,Quasiconvex function,Nonlinear programming,Binary search algorithm,Bioinformatics,Solver,Constraint based modeling,Maximization,Speedup
Journal
Volume
Issue
ISSN
17
1
1471-2105
Citations 
PageRank 
References 
1
0.37
6
Authors
6
Name
Order
Citations
PageRank
Laurence Yang111.72
Ding Ma210.37
Ali Ebrahim3604.31
Colton J Lloyd452.51
Michael A. Saunders51224785.45
Bernhard O. Palsson675167.99