To understand how cells control and exploit biochemical fluctuations, we must identify the sources of stochasticity, quantify their effects, and distinguish informative variation from confounding "noise." We present an analysis that allows fluctuations of biochemical networks to be decomposed into multiple components, gives conditions for the design of experimental reporters to measure all components, and provides a technique to predict the magnitude of these components from models. Further, we identify a particular component of variation that can be used to quantify the efficacy of information flow through a biochemical network. By applying our approach to osmosensing in yeast, we can predict the probability of the different osmotic conditions experienced by wild-type yeast and show that the majority of variation can be informational if we include variation generated in response to the cellular environment. Our results are fundamental to quantifying sources of variation and thus are a means to understand biological "design."
- analysis of variance
- internal history
- gene expression
- signal transduction
- intrinsic and extrinsic noise