Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

Derek N. Macklin; Travis A. Ahn-Horst; Heejo Choi; Nicholas A. Ruggero; Javier Carrera; John C. Mason; Gwanggyu Sun; Eran Agmon; Mialy M. DeFelice; Inbal Maayan; Keara Lane; Ryan K. Spangler; Taryn E. Gillies; Morgan L. Paull; Sajia Akhter; Samuel R. Bray; Daniel S. Weaver; Ingrid M. Keseler; Peter D. Karp; Jerry H. Morrison; Markus W. Covert

doi:10.1126/science.aav3751

Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

Derek N. Macklin, Travis A. Ahn-Horst, Heejo Choi, Nicholas A. Ruggero, Javier Carrera, John C. Mason, Gwanggyu Sun, Eran Agmon, Mialy M. DeFelice, Inbal Maayan, Keara Lane, Ryan K. Spangler, Taryn E. Gillies, Morgan L. Paull, Sajia Akhter, Samuel R. Bray, Daniel S. Weaver, Ingrid M. Keseler, Peter D. Karp, Jerry H. MorrisonMarkus W. Covert^*

^*Corresponding author for this work

Molecular Biosciences

Research output: Contribution to journal › Article › peer-review

103 Scopus citations

Abstract

The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

Original language	English (US)
Article number	eaav3751
Journal	Science
Volume	369
Issue number	6502
DOIs	https://doi.org/10.1126/science.aav3751
State	Published - Jul 24 2020

Funding

This work was supported by the Paul G. Allen Frontiers Group through the Allen Discovery Center at Stanford; the Stanford Center for Systems Biology (NIH P50GM107615); an NIH Director's Pioneer Award (5DP1LM01150); an Allen Distinguished Investigator award to M.W.C.; a Stanford Graduate Fellowship, DOE Computational Science Graduate Fellowship (DE-FG02-97ER25308) and Siebel Scholarship to D.N.M.; an NSF Graduate Research Fellowship to N.A.R. and M.L.P.; a Stanford School of Medicine Dean's Postdoctoral Fellowship to J.C.; a Stanford Graduate Fellowship and NSF Graduate Research Fellowship to H.C.; an Agilent Graduate Student Fellowship and Stanford NIST JIMB Training Grant Graduate Fellowship (70NANB15H192) to J.C.M.; and an NIH grant (GM077678) to P.D.K.

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Macklin, D. N., Ahn-Horst, T. A., Choi, H., Ruggero, N. A., Carrera, J., Mason, J. C., Sun, G., Agmon, E., DeFelice, M. M., Maayan, I., Lane, K., Spangler, R. K., Gillies, T. E., Paull, M. L., Akhter, S., Bray, S. R., Weaver, D. S., Keseler, I. M., Karp, P. D., ... Covert, M. W. (2020). Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation. Science, 369(6502), Article eaav3751. https://doi.org/10.1126/science.aav3751

@article{a8d673182be44b3682a4eb8f9a7bbaa9,

title = "Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation",

abstract = "The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.",

author = "Macklin, {Derek N.} and Ahn-Horst, {Travis A.} and Heejo Choi and Ruggero, {Nicholas A.} and Javier Carrera and Mason, {John C.} and Gwanggyu Sun and Eran Agmon and DeFelice, {Mialy M.} and Inbal Maayan and Keara Lane and Spangler, {Ryan K.} and Gillies, {Taryn E.} and Paull, {Morgan L.} and Sajia Akhter and Bray, {Samuel R.} and Weaver, {Daniel S.} and Keseler, {Ingrid M.} and Karp, {Peter D.} and Morrison, {Jerry H.} and Covert, {Markus W.}",

note = "Funding Information: This work was supported by the Paul G. Allen Frontiers Group through the Allen Discovery Center at Stanford; the Stanford Center for Systems Biology (NIH P50GM107615); an NIH Director's Pioneer Award (5DP1LM01150); an Allen Distinguished Investigator award to M.W.C.; a Stanford Graduate Fellowship, DOE Computational Science Graduate Fellowship (DE-FG02-97ER25308) and Siebel Scholarship to D.N.M.; an NSF Graduate Research Fellowship to N.A.R. and M.L.P.; a Stanford School of Medicine Dean's Postdoctoral Fellowship to J.C.; a Stanford Graduate Fellowship and NSF Graduate Research Fellowship to H.C.; an Agilent Graduate Student Fellowship and Stanford NIST JIMB Training Grant Graduate Fellowship (70NANB15H192) to J.C.M.; and an NIH grant (GM077678) to P.D.K. Publisher Copyright: {\textcopyright} 2020 American Association for the Advancement of Science. All rights reserved.",

year = "2020",

month = jul,

day = "24",

doi = "10.1126/science.aav3751",

language = "English (US)",

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Macklin, DN, Ahn-Horst, TA, Choi, H, Ruggero, NA, Carrera, J, Mason, JC, Sun, G, Agmon, E, DeFelice, MM, Maayan, I, Lane, K, Spangler, RK, Gillies, TE, Paull, ML, Akhter, S, Bray, SR, Weaver, DS, Keseler, IM, Karp, PD, Morrison, JH & Covert, MW 2020, 'Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation', Science, vol. 369, no. 6502, eaav3751. https://doi.org/10.1126/science.aav3751

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T1 - Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

AU - Macklin, Derek N.

AU - Ahn-Horst, Travis A.

AU - Choi, Heejo

AU - Ruggero, Nicholas A.

AU - Carrera, Javier

AU - Mason, John C.

AU - Sun, Gwanggyu

AU - Agmon, Eran

AU - DeFelice, Mialy M.

AU - Maayan, Inbal

AU - Lane, Keara

AU - Spangler, Ryan K.

AU - Gillies, Taryn E.

AU - Paull, Morgan L.

AU - Akhter, Sajia

AU - Bray, Samuel R.

AU - Weaver, Daniel S.

AU - Keseler, Ingrid M.

AU - Karp, Peter D.

AU - Morrison, Jerry H.

AU - Covert, Markus W.

N1 - Funding Information: This work was supported by the Paul G. Allen Frontiers Group through the Allen Discovery Center at Stanford; the Stanford Center for Systems Biology (NIH P50GM107615); an NIH Director's Pioneer Award (5DP1LM01150); an Allen Distinguished Investigator award to M.W.C.; a Stanford Graduate Fellowship, DOE Computational Science Graduate Fellowship (DE-FG02-97ER25308) and Siebel Scholarship to D.N.M.; an NSF Graduate Research Fellowship to N.A.R. and M.L.P.; a Stanford School of Medicine Dean's Postdoctoral Fellowship to J.C.; a Stanford Graduate Fellowship and NSF Graduate Research Fellowship to H.C.; an Agilent Graduate Student Fellowship and Stanford NIST JIMB Training Grant Graduate Fellowship (70NANB15H192) to J.C.M.; and an NIH grant (GM077678) to P.D.K. Publisher Copyright: © 2020 American Association for the Advancement of Science. All rights reserved.

PY - 2020/7/24

Y1 - 2020/7/24

N2 - The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

AB - The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various groups over decades. We identified inconsistencies with functional consequences across the data, including that the total output of the ribosomes and RNA polymerases described by data are not sufficient for a cell to reproduce measured doubling times, that measured metabolic parameters are neither fully compatible with each other nor with overall growth, and that essential proteins are absent during the cell cycle-and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.

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DO - 10.1126/science.aav3751

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AN - SCOPUS:85088511361

SN - 0036-8075

VL - 369

JO - Science

JF - Science

IS - 6502

M1 - eaav3751

ER -

Simultaneous cross-evaluation of heterogeneous E. coli datasets via mechanistic simulation

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