Environmental sensing, information transfer, and cellular decision-making

• We discuss current theoretical approaches to the quantitative study of cellular decision-making.
• We focus on the application of information theory, sequential inference, decision theory, and optimality arguments.
• We consider interpretations of mutual information in systems biology versus engineering.

The recognition that gene expression can be substantially stochastic poses the question of how cells respond to dynamic environments using biochemistry that itself fluctuates. The study of cellular decision-making aims to solve this puzzle by focusing on quantitative understanding of the variation seen across isogenic populations in response to extracellular change. This behaviour is complex, and a theoretical framework within which to embed experimental results is needed. Here we review current approaches, with an emphasis on information theory, sequential data processing, and optimality arguments. We conclude by highlighting some limitations of these techniques and the importance of connecting both theory and experiment to measures of fitness.

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