Abstract
BackgroundGenome-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.ResultsHere, 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.ConclusionsJust 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1240-1) contains supplementary material, which is available to authorized users.
Highlights
Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling
SolveME: Combined solution procedure for growth maximization nonlinear programming (NLP) Because Quad MINOS converges faster when μ0 is closer to the optimum, we developed a combined solution procedure that uses a coarse bisection via bisectME to identify μ0 < μ∗, provides the corresponding basis to warm-start Quad MINOS on the NLP
We developed an efficient methodology for solving nonlinear, multiscale models of metabolism and macromolecule expression (ME models)
Summary
Genome-scale models of metabolism and macromolecular expression (ME) significantly expand the scope and predictive capabilities of constraint-based modeling. Metabolic network reconstructions are used to compute reaction fluxes at pseudo steady-state by solving the linear program, max cT v v s.t. Sv = 0, vL ≤ v ≤ vU , Recently, Lerman et al [3] developed the first integrated genome-scale reconstruction of Metabolism and macromolecular Expression (ME) for the microorganism Thermotoga maritima. Sv = 0, vL ≤ v ≤ vU , Recently, Lerman et al [3] developed the first integrated genome-scale reconstruction of Metabolism and macromolecular Expression (ME) for the microorganism Thermotoga maritima This ME model described the transcription and translation machinery associated with 651 genes and the metabolic network catalyzed by the enzymes synthesized in the model. The latest ME models for E. coli account for 80 % of the proteome by mass [5], enable computation of proteome allocation shifts between conditions [7], and predict the macromolecular composition of the cell [8]
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