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

Research output: Contribution to journalArticle

3 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 languageEnglish (US)
Article numbereaav3751
JournalScience
Volume369
Issue number6502
DOIs
StatePublished - Jul 24 2020

ASJC Scopus subject areas

  • General

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