A predictive model for transcriptional control of physiology in a free living cell
A predictive model for transcriptional control of physiology in a free living cell
Bonneau, R. et al. Cell 131, 1354–1365 (2007)
Extracting meaningful information about microbial metabolism and gene regulation from genome sequences, to engineer new solutions to biotechnological and medical problems, is often confounded by a lack of information on the basic biology of sequenced microorganisms. A team led by Nitin Baliga integrated the results of multiple microarray and proteomics experiments (carried out under a range of environmental conditions) with information on protein-structure predictions and insights
from gene knockouts to produce a network model for the prediction of cellular responses in the poorly characterized halophile Halobacterium salinarum NRC-1. The resulting model recapitulates known metabolic processes for ~80% of the genes and its validity was established by predicting transcriptional responses to stimuli and then confirming these data experimentally. This showcases the power of systems approaches for rapidly analysing the biology of sequenced microorganisms.