期刊
SCIENCE
卷 369, 期 6502, 页码 390-+出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/science.aav3751
关键词
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资金
- Paul G. Allen Frontiers Group through the Allen Discovery Center at Stanford
- Stanford Center for Systems Biology [NIH P50GM107615]
- NIH Director's Pioneer Award [5DP1LM01150]
- Allen Distinguished Investigator award
- Stanford Graduate Fellowship
- DOE Computational Science Graduate Fellowship [DE-FG02-97ER25308]
- Siebel Scholarship
- NSF
- Stanford School of Medicine Dean's Postdoctoral Fellowship
- Agilent Graduate Student Fellowship
- Stanford NIST JIMB Training Grant Graduate Fellowship [70NANB15H192]
- NIH [GM077678]
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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