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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 labs over decades. We identified
inconsistencies with functional consequences across the data, including: that the data describing total output
of the ribosomes and RNA polymerases is 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; 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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