Large scale physiological readjustment during growth enables rapid, comprehensive and inexpensive systems analysis

We have discovered in the model organism Halobacterium salinarum NRC-1 that batch culturing in complex medium stimulates meaningful changes in the expression of approximately two thirds of all genes. While the majority of these changes occur during transition from rapid exponential growth to the stationary phase, several transient physiological states were detected beyond what has been previously observed. In sum, integrated analysis of transcript and metabolite changes has helped uncover growth phase-associated physiologies, operational interrelationships among two thirds of all genes, specialized functions for gene family members, waves of transcription factor activities, and growth phase associated cell morphology control.Simple laboratory culturing in complex medium can be enormously informative regarding the activities of and interrelationships among a large fraction of all genes in an organism. This also yields important baseline physiological context for designing specific perturbation experiments at different phases of growth. The integration of such growth and perturbation studies with measurements of associated environmental factor changes is a practical and economical route for the elucidation of comprehensive systems-level models of biological systems.One of the main goals of current molecular systems biology is to generate quantitative models for the structure and behavior of cellular, metabolic, signal transducing and gene regulatory networks in a complete organism. Such knowledge may enable us to ultimately predict global changes in gene expression of an organism in response to cellular stimuli and enable the predictable reengineering of cells for therapeutic or industrial purposes [1]. The relative simplicity of microbial genomes and the shrinking cost of measuring their complete transcriptomes has made deciphering structures of gene regulatory influence networks and detecting functional relationships within nearly genomically-complete data sets feas