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

Ma, Ding; Yang, Laurence; Fleming, Ronan M.  T.; Thiele, Ines; Palsson, Bernhard O.; Saunders, Michael A.

doi:10.1038/srep40863

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

Journal Article · Wed Jan 18 00:00:00 EST 2017 · Scientific Reports

DOI:https://doi.org/10.1038/srep40863· OSTI ID:1347391

Ma, Ding ^[1]; Yang, Laurence ^[2]; Fleming, Ronan M. T. ^[3]; Thiele, Ines ^[3]; Palsson, Bernhard O. ^[4]; Saunders, Michael A. ^[1]

Stanford Univ., CA (United States). Dept. of Management Science and Engineering
Univ. of California, San Diego, CA (United States). Dept. of Bioengineering
Univ. of Luxembourg, Esch-sur-Alzette (Luxembourg). Luxembourg Centre for System Biomedicine
Univ. of California, San Diego, CA (United States). Dept. of Bioengineering; Technical Univ. of Denmark, Horsholm (Denmark). Novo Nordisk Foundation Center for Biosustainability

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.

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Research Organization:: Univ. of Luxembourg, Esch-sur-Alzette (Luxembourg)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0010429

OSTI ID:: 1347391

Journal Information:: Scientific Reports, Vol. 7; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 20 works

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The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models Salvy, Pierre; Hatzimanikatis, Vassily Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-019-13818-7	journal	January 2020
Cellular responses to reactive oxygen species are predicted from molecular mechanisms Yang, Laurence; Mih, Nathan; Anand, Amitesh Proceedings of the National Academy of Sciences, Vol. 116, Issue 28 https://doi.org/10.1073/pnas.1905039116	journal	July 2019
The genetic basis for adaptation of model-designed syntrophic co-cultures Lloyd, Colton J.; King, Zachary A.; Sandberg, Troy E. PLOS Computational Biology, Vol. 15, Issue 3 https://doi.org/10.1371/journal.pcbi.1006213	journal	March 2019
COBRAme: A computational framework for genome-scale models of metabolism and gene expression Lloyd, Colton J.; Ebrahim, Ali; Yang, Laurence PLOS Computational Biology, Vol. 14, Issue 7 https://doi.org/10.1371/journal.pcbi.1006302	journal	July 2018
Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community Pinu, Farhana R.; Beale, David J.; Paten, Amy M. Metabolites, Vol. 9, Issue 4 https://doi.org/10.3390/metabo9040076	journal	April 2019
Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers Heckmann, David; Campeau, Anaamika; Lloyd, Colton J. Proceedings of the National Academy of Sciences, Vol. 117, Issue 37 https://doi.org/10.1073/pnas.2001562117	journal	September 2020
Biopython: freely available Python tools for computational molecular biology and bioinformatics Cock, P. J. A.; Antao, T.; Chang, J. T. Bioinformatics, Vol. 25, Issue 11 https://doi.org/10.1093/bioinformatics/btp163	journal	March 2009
DynamicME: dynamic simulation and refinement of integrated models of metabolism and protein expression Yang, Laurence; Ebrahim, Ali; Lloyd, Colton J. BMC Systems Biology, Vol. 13, Issue 1 https://doi.org/10.1186/s12918-018-0675-6	journal	January 2019

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