Metabolic and regulatory networks may be expanded by coupling high-throughput phenotyping and gene expression data with the predictions of a computational model. (Reprinted with permission,
Most people like their predictions to pan out, but Markus Covert is glad when his fail. That's because he has developed a genome-scale mathematical model of the transcriptional interactions that regulate bacterial metabolism. The model's mistakes lead to new ideas about how the network is put together.
Covert, now a postdoctoral fellow in David Baltimore's lab at the California Institute of Technology, constructed the Escherichia coli model, described in Nature,1 when he was a doctoral student with Bernhard Palsson at UC-San Diego. "We have the bacterium's full genotype and can observe its phenotypes, but there is a gap in-between," says Palsson, professor of bio-engineering. "That gap is filled by the interactions of thousands of compounds and macromolecules. Models are needed to deal ...
