BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data

Jean Christophe Lachance, Colton J. Lloyd, Jonathan M. Monk, Laurence Yang, Anand V. Sastry, Yara Seif, Bernhard O. Palsson, Sébastien Rodrigue, Adam M. Feist, Zachary A. King, Pierre Étienne Jacques

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Abstract

Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).
Original languageEnglish
Article numbere1006971
JournalPLOS Computational Biology
Volume15
Issue number4
ISSN1553-7358
DOIs
Publication statusPublished - 2019

Cite this

Lachance, J. C., Lloyd, C. J., Monk, J. M., Yang, L., Sastry, A. V., Seif, Y., ... Jacques, P. É. (2019). BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. PLOS Computational Biology, 15(4), [e1006971]. https://doi.org/10.1371/journal.pcbi.1006971
Lachance, Jean Christophe ; Lloyd, Colton J. ; Monk, Jonathan M. ; Yang, Laurence ; Sastry, Anand V. ; Seif, Yara ; Palsson, Bernhard O. ; Rodrigue, Sébastien ; Feist, Adam M. ; King, Zachary A. ; Jacques, Pierre Étienne. / BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. In: PLOS Computational Biology. 2019 ; Vol. 15, No. 4.
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abstract = "Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).",
author = "Lachance, {Jean Christophe} and Lloyd, {Colton J.} and Monk, {Jonathan M.} and Laurence Yang and Sastry, {Anand V.} and Yara Seif and Palsson, {Bernhard O.} and S{\'e}bastien Rodrigue and Feist, {Adam M.} and King, {Zachary A.} and Jacques, {Pierre {\'E}tienne}",
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Lachance, JC, Lloyd, CJ, Monk, JM, Yang, L, Sastry, AV, Seif, Y, Palsson, BO, Rodrigue, S, Feist, AM, King, ZA & Jacques, PÉ 2019, 'BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data', PLOS Computational Biology, vol. 15, no. 4, e1006971. https://doi.org/10.1371/journal.pcbi.1006971

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data. / Lachance, Jean Christophe; Lloyd, Colton J.; Monk, Jonathan M.; Yang, Laurence; Sastry, Anand V.; Seif, Yara; Palsson, Bernhard O.; Rodrigue, Sébastien; Feist, Adam M.; King, Zachary A.; Jacques, Pierre Étienne.

In: PLOS Computational Biology, Vol. 15, No. 4, e1006971, 2019.

Research output: Contribution to journalJournal articleResearchpeer-review

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T1 - BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data

AU - Lachance, Jean Christophe

AU - Lloyd, Colton J.

AU - Monk, Jonathan M.

AU - Yang, Laurence

AU - Sastry, Anand V.

AU - Seif, Yara

AU - Palsson, Bernhard O.

AU - Rodrigue, Sébastien

AU - Feist, Adam M.

AU - King, Zachary A.

AU - Jacques, Pierre Étienne

PY - 2019

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N2 - Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).

AB - Genome-scale metabolic models (GEMs) are mathematically structured knowledge bases of metabolism that provide phenotypic predictions from genomic information. GEM-guided predictions of growth phenotypes rely on the accurate definition of a biomass objective function (BOF) that is designed to include key cellular biomass components such as the major macromolecules (DNA, RNA, proteins), lipids, coenzymes, inorganic ions and species-specific components. Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a Biomass Objective Function from experimental data. BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. We used BOFdat to reconstruct the BOF of the Escherichia coli model iML1515, a gold standard in the field. The BOF generated by BOFdat resulted in the most concordant biomass composition, growth rate, and gene essentiality prediction accuracy when compared to other methods. Installation instructions for BOFdat are available in the documentation and the source code is available on GitHub (https://github.com/jclachance/BOFdat).

U2 - 10.1371/journal.pcbi.1006971

DO - 10.1371/journal.pcbi.1006971

M3 - Journal article

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VL - 15

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-7358

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