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Title: Do genome-scale models need exact solvers or clearer standards?

Abstract

Constraint-based analysis of genome-scale models (GEMs) arose shortly after the first genome sequences became available. As numerous reviews of the field show, this approach and methodology has proven to be successful in studying a wide range of biological phenomena (McCloskey et al, 2013; Bordbar et al, 2014). However, efforts to expand the user base are impeded by hurdles in correctly formulating these problems to obtain numerical solutions. In particular, in a study entitled “An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models” (Chindelevitch et al, 2014), the authors apply an exact solver to 88 genome-scale constraint-based models of metabolism. The authors claim that COBRA calculations (Orth et al, 2010) are inconsistent with their results and that many published and actively used (Lee et al, 2007; McCloskey et al, 2013) genome-scale models do support cellular growth in existing studies only because of numerical errors. They base these broad claims on two observations: (i) three reconstructions (iAF1260, iIT341, and iNJ661) compute feasibly in COBRA, but are infeasible when exact numerical algorithms are used by their software (entitled MONGOOSE); (ii) linear programs generated by MONGOOSE for iIT341 were submitted to the NEOS Server (a Web site thatmore » runs linear programs through various solvers) and gave inconsistent results. They further claim that a large percentage of these COBRA models are actually unable to produce biomass flux. Here, we demonstrate that the claims made by Chindelevitch et al (2014) stem from an incorrect parsing of models from files rather than actual problems with numerical error or COBRA computations.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [1];  [3];  [9];  [10];  [11];  [12];  [13];  [14];  [15];  [16];  [17];  [1] more »;  [1];  [18];  [19];  [20];  [5];  [20];  [21];  [22];  [23];  [3];  [24];  [1];  [25];  [26];  [27];  [28];  [18];  [29];  [11];  [18];  [3] « less
+ Show Author Affiliations
  1. Univ. of California, San Diego, CA (United States)
  2. Norwegian University of Science and Technology (NTNU), Trondheim (Norway)
  3. University of Luxembourg, Belval (Luxembourg)
  4. Sinopia Biosciences Inc., San Diego, CA (United States)
  5. Genomatica, Inc., San Diego, CA (United States)
  6. Harvard Medical School, Boston, MA (United States)
  7. Univ. of California, San Diego, CA (United States); University of Tuebingen (Germany)
  8. Intrexon, Inc., San Diego, CA (United States)
  9. Virginia Commonwealth Univ., Richmond, VA (United States)
  10. Ecole Polytechnique Fédérale de Lausanne (Switzerland)
  11. Technical University of Denmark, Lyngby (Denmark)
  12. Rose‐Hulman Institute of Technology, Terre Haute, IN (United States)
  13. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  14. Utah State Univ., Logan, UT (United States)
  15. Univ. of California, Los Angeles, CA (United States)
  16. Technical University of Denmark, Lyngby (Denmark) ; Korea Advanced Institute of Science and Technology (KAIST), Daejeon (Korea)
  17. Babraham Institute, Cambridge (United Kingdom)
  18. Stanford Univ., CA (United States)
  19. Univ. of Toronto, ON (Canada)
  20. Pennsylvania State Univ., University Park, PA (United States)
  21. Technical University of Denmark, Lyngby (Denmark); Chalmers University of Technology, Gothenburg (Sweden)
  22. Univ. of Queensland, Brisbane (Australia)
  23. Centro de Investigaciones Biológicas (CSIC), Madrid (Spain)
  24. Biological Research Center, Szeged (Hungary)
  25. Univ. of Virginia, Charlottesville, VA (United States)
  26. European Molecular Biology Laboratory, Heidelberg (Germany)
  27. Institute for Systems Biology, Seattle, WA (United States)
  28. University of Wisconsin‐Madison, Madison, WI (United States)
  29. Virginia Polytechnic Inst. and State Univ. (Virginia Tech), Blacksburg, VA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1497970
Report Number(s):
LLNL-JRNL-752771
Journal ID: ISSN 1744-4292; 938708
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Molecular Systems Biology
Additional Journal Information:
Journal Volume: 11; Journal Issue: 10; Journal ID: ISSN 1744-4292
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING

Citation Formats

Ebrahim, A., Almaas, E., Bauer, E., Bordbar, A., Burgard, A. P., Chang, R. L., Drager, A., Famili, I., Feist, A. M., Fleming, R. M., Fong, S. S., Hatzimanikatis, V., Herrgard, M. J., Holder, A., Hucka, M., Hyduke, D., Jamshidi, N., Lee, S. Y., Le Novere, N., Lerman, J. A., Lewis, N. E., Ma, D., Mahadevan, R., Maranas, C., Nagarajan, H., Navid, A., Nielsen, J., Nielsen, L. K., Nogales, J., Noronha, A., Pal, C., Palsson, B. O., Papin, J. A., Patil, K. R., Price, N. D., Reed, J. L., Saunders, M., Senger, R. S., Sonnenschein, N., Sun, Y., and Thiele, I. Do genome-scale models need exact solvers or clearer standards?. United States: N. p., 2015. Web. doi:10.15252/msb.20156157.
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Ebrahim, A., Almaas, E., Bauer, E., Bordbar, A., Burgard, A. P., Chang, R. L., Drager, A., Famili, I., Feist, A. M., Fleming, R. M., Fong, S. S., Hatzimanikatis, V., Herrgard, M. J., Holder, A., Hucka, M., Hyduke, D., Jamshidi, N., Lee, S. Y., Le Novere, N., Lerman, J. A., Lewis, N. E., Ma, D., Mahadevan, R., Maranas, C., Nagarajan, H., Navid, A., Nielsen, J., Nielsen, L. K., Nogales, J., Noronha, A., Pal, C., Palsson, B. O., Papin, J. A., Patil, K. R., Price, N. D., Reed, J. L., Saunders, M., Senger, R. S., Sonnenschein, N., Sun, Y., & Thiele, I. Do genome-scale models need exact solvers or clearer standards?. United States. https://doi.org/10.15252/msb.20156157
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Ebrahim, A., Almaas, E., Bauer, E., Bordbar, A., Burgard, A. P., Chang, R. L., Drager, A., Famili, I., Feist, A. M., Fleming, R. M., Fong, S. S., Hatzimanikatis, V., Herrgard, M. J., Holder, A., Hucka, M., Hyduke, D., Jamshidi, N., Lee, S. Y., Le Novere, N., Lerman, J. A., Lewis, N. E., Ma, D., Mahadevan, R., Maranas, C., Nagarajan, H., Navid, A., Nielsen, J., Nielsen, L. K., Nogales, J., Noronha, A., Pal, C., Palsson, B. O., Papin, J. A., Patil, K. R., Price, N. D., Reed, J. L., Saunders, M., Senger, R. S., Sonnenschein, N., Sun, Y., and Thiele, I. Wed . "Do genome-scale models need exact solvers or clearer standards?". United States. https://doi.org/10.15252/msb.20156157. https://www.osti.gov/servlets/purl/1497970.
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@article{osti_1497970,
title = {Do genome-scale models need exact solvers or clearer standards?},
author = {Ebrahim, A. and Almaas, E. and Bauer, E. and Bordbar, A. and Burgard, A. P. and Chang, R. L. and Drager, A. and Famili, I. and Feist, A. M. and Fleming, R. M. and Fong, S. S. and Hatzimanikatis, V. and Herrgard, M. J. and Holder, A. and Hucka, M. and Hyduke, D. and Jamshidi, N. and Lee, S. Y. and Le Novere, N. and Lerman, J. A. and Lewis, N. E. and Ma, D. and Mahadevan, R. and Maranas, C. and Nagarajan, H. and Navid, A. and Nielsen, J. and Nielsen, L. K. and Nogales, J. and Noronha, A. and Pal, C. and Palsson, B. O. and Papin, J. A. and Patil, K. R. and Price, N. D. and Reed, J. L. and Saunders, M. and Senger, R. S. and Sonnenschein, N. and Sun, Y. and Thiele, I.},
abstractNote = {Constraint-based analysis of genome-scale models (GEMs) arose shortly after the first genome sequences became available. As numerous reviews of the field show, this approach and methodology has proven to be successful in studying a wide range of biological phenomena (McCloskey et al, 2013; Bordbar et al, 2014). However, efforts to expand the user base are impeded by hurdles in correctly formulating these problems to obtain numerical solutions. In particular, in a study entitled “An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models” (Chindelevitch et al, 2014), the authors apply an exact solver to 88 genome-scale constraint-based models of metabolism. The authors claim that COBRA calculations (Orth et al, 2010) are inconsistent with their results and that many published and actively used (Lee et al, 2007; McCloskey et al, 2013) genome-scale models do support cellular growth in existing studies only because of numerical errors. They base these broad claims on two observations: (i) three reconstructions (iAF1260, iIT341, and iNJ661) compute feasibly in COBRA, but are infeasible when exact numerical algorithms are used by their software (entitled MONGOOSE); (ii) linear programs generated by MONGOOSE for iIT341 were submitted to the NEOS Server (a Web site that runs linear programs through various solvers) and gave inconsistent results. They further claim that a large percentage of these COBRA models are actually unable to produce biomass flux. Here, we demonstrate that the claims made by Chindelevitch et al (2014) stem from an incorrect parsing of models from files rather than actual problems with numerical error or COBRA computations.},
doi = {10.15252/msb.20156157},
journal = {Molecular Systems Biology},
number = 10,
volume = 11,
place = {United States},
year = {Wed Oct 14 00:00:00 EDT 2015},
month = {Wed Oct 14 00:00:00 EDT 2015}
}
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    iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE
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    Personalized Cardiovascular Disease Prediction and Treatment—A Review of Existing Strategies and Novel Systems Medicine Tools
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    MEMOTE for standardized genome-scale metabolic model testing
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    Inferring Biochemical Reactions and Metabolite Structures to Understand Metabolic Pathway Drift
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    Evaluation of reaction gap-filling accuracy by randomization
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    ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions
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    A community-driven reconstruction of the Aspergillus niger metabolic network
    journal, September 2018

    • Brandl, Julian; Aguilar-Pontes, Maria Victoria; Schäpe, Paul
    • Fungal Biology and Biotechnology, Vol. 5, Issue 1
    • DOI: 10.1186/s40694-018-0060-7

    SteadyCom: Predicting microbial abundances while ensuring community stability
    journal, May 2017

    • Chan, Siu Hung Joshua; Simons, Margaret N.; Maranas, Costas D.
    • PLOS Computational Biology, Vol. 13, Issue 5
    • DOI: 10.1371/journal.pcbi.1005539

    COBRAme: A computational framework for genome-scale models of metabolism and gene expression
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    Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes
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    MEMOTE for standardized genome-scale metabolic model testing
    text, January 2020

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    iCN718, an Updated and Improved Genome-Scale Metabolic Network Reconstruction of Acinetobacter baumannii AYE
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    Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells
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    • Sen, Partho; Kemppainen, Esko; Orešič, Matej
    • Frontiers in Molecular Biosciences, Vol. 4
    • DOI: 10.3389/fmolb.2017.00096

    Personalized Cardiovascular Disease Prediction and Treatment—A Review of Existing Strategies and Novel Systems Medicine Tools
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