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.
Prof. Markus Covert’s focus is on building computational models of complex biological processes, and using these models to guide an experimental program. Such an approach leads to a relatively rapid identification and validation of previously unknown components and interactions. Biological systems of interest include metabolic, regulatory and signaling networks as well as cell-cell interactions. Current research involves the dynamic behavior of NF-kappaB, an important family of transcription factors whose aberrant activity has been linked to oncogenesis, tumor progression, and resistance to chemotherapy.
For details of his research and recent publication, please visit HERE
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