solveME: fast and reliable solution of nonlinear ME models

Laurence Yang; Ding Ma; Ali Ebrahim; Colton J. Lloyd; Michael A. Saunders; Bernhard Palsson

doi:10.1186/s12859-016-1240-1

solveME: fast and reliable solution of nonlinear ME models

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

University of California at San Diego
Stanford University

Research output: Contribution to journal › Journal article › Research › peer-review

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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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1240-1) contains supplementary material, which is available to authorized users.

Original language	English
Article number	391
Journal	B M C Bioinformatics
Volume	17
Issue number	1
Number of pages	10
ISSN	1471-2105
DOIs	https://doi.org/10.1186/s12859-016-1240-1
Publication status	Published - 2016

Bibliographical note

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Keywords

Nonlinear optimization
Constraint-based modeling
Metabolism
Proteome
Quasiconvex

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title = "solveME: fast and reliable solution of nonlinear ME models",

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{\texttimes} 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1240-1) contains supplementary material, which is available to authorized users. ",

keywords = "Nonlinear optimization, Constraint-based modeling, Metabolism, Proteome, Quasiconvex",

author = "Laurence Yang and Ding Ma and Ali Ebrahim and Lloyd, \{Colton J.\} and Saunders, \{Michael A.\} and Bernhard Palsson",

note = "This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.",

year = "2016",

doi = "10.1186/s12859-016-1240-1",

language = "English",

volume = "17",

journal = "B M C Bioinformatics",

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AU - Yang, Laurence

AU - Ma, Ding

AU - Ebrahim, Ali

AU - Lloyd, Colton J.

AU - Saunders, Michael A.

AU - Palsson, Bernhard

N1 - This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

PY - 2016

Y1 - 2016

N2 - 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1240-1) contains supplementary material, which is available to authorized users.

AB - 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. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1240-1) contains supplementary material, which is available to authorized users.

KW - Nonlinear optimization

KW - Constraint-based modeling

KW - Metabolism

KW - Proteome

KW - Quasiconvex

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DO - 10.1186/s12859-016-1240-1

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C2 - 27659412

SN - 1471-2105

VL - 17

JO - B M C Bioinformatics

JF - B M C Bioinformatics

IS - 1

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ER -

solveME: fast and reliable solution of nonlinear ME models

Abstract

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