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Title: Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression

Abstract

Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.

Authors:
 [1];  [2];  [3];  [3];  [4];  [1]
  1. Stanford Univ., CA (United States). Dept. of Management Science and Engineering
  2. Univ. of California, San Diego, CA (United States). Dept. of Bioengineering
  3. Univ. of Luxembourg, Esch-sur-Alzette (Luxembourg). Luxembourg Centre for System Biomedicine
  4. Univ. of California, San Diego, CA (United States). Dept. of Bioengineering; Technical Univ. of Denmark, Horsholm (Denmark). Novo Nordisk Foundation Center for Biosustainability
Publication Date:
Research Org.:
Univ. of Luxembourg, Esch-sur-Alzette (Luxembourg)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1347391
Grant/Contract Number:
SC0010429
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Ma, Ding, Yang, Laurence, Fleming, Ronan M. T., Thiele, Ines, Palsson, Bernhard O., and Saunders, Michael A. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression. United States: N. p., 2017. Web. doi:10.1038/srep40863.
Ma, Ding, Yang, Laurence, Fleming, Ronan M. T., Thiele, Ines, Palsson, Bernhard O., & Saunders, Michael A. Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression. United States. doi:10.1038/srep40863.
Ma, Ding, Yang, Laurence, Fleming, Ronan M. T., Thiele, Ines, Palsson, Bernhard O., and Saunders, Michael A. Wed . "Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression". United States. doi:10.1038/srep40863. https://www.osti.gov/servlets/purl/1347391.
@article{osti_1347391,
title = {Reliable and efficient solution of genome-scale models of Metabolism and macromolecular Expression},
author = {Ma, Ding and Yang, Laurence and Fleming, Ronan M. T. and Thiele, Ines and Palsson, Bernhard O. and Saunders, Michael A.},
abstractNote = {Currently, Constraint-Based Reconstruction and Analysis (COBRA) is the only methodology that permits integrated modeling of Metabolism and macromolecular Expression (ME) at genome-scale. Linear optimization computes steady-state flux solutions to ME models, but flux values are spread over many orders of magnitude. Data values also have greatly varying magnitudes. Furthermore, standard double-precision solvers may return inaccurate solutions or report that no solution exists. Exact simplex solvers based on rational arithmetic require a near-optimal warm start to be practical on large problems (current ME models have 70,000 constraints and variables and will grow larger). We also developed a quadrupleprecision version of our linear and nonlinear optimizer MINOS, and a solution procedure (DQQ) involving Double and Quad MINOS that achieves reliability and efficiency for ME models and other challenging problems tested here. DQQ will enable extensive use of large linear and nonlinear models in systems biology and other applications involving multiscale data.},
doi = {10.1038/srep40863},
journal = {Scientific Reports},
number = ,
volume = 7,
place = {United States},
year = {Wed Jan 18 00:00:00 EST 2017},
month = {Wed Jan 18 00:00:00 EST 2017}
}

Journal Article:
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