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One of the important goals of biol. is to understand the relationship between DNA sequence information and nonlinear cellular responses. This relationship is central to the ability to effectively engineer cellular phenotypes, pathways, and characteristics. Expression arrays for monitoring total gene expression based on mRNA can provide quant. insight into which gene or genes are on or off; but this information is insufficient to fully predict dynamic biol. phenomena. Using nonlinear stability anal. we show that a combination of gene expression information at the message level and at the protein level is required to describe even simple models of gene networks. To help illustrate the need for such information we consider a mechanistic model for circadian rhythmicity which shows agreement with exptl. observations when protein and mRNA information are included and we propose a framework for acquiring and analyzing exptl. and math. derived information about gene networks. (c) 1999 Academic Press. [on SciFinder (R)]