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2012 Integrative Biology  
Cells make many binary (all-or-nothing) decisions based on noisy signals gathered from their environment and processed through noisy decision-making pathways. Reducing the effect of noise to improve the fidelity of decision-making comes at the expense of increased complexity, creating a tradeoff between performance and metabolic cost. We present a framework based on rate distortion theory, a branch of information theory, to quantify this tradeoff and design binary decision-making strategies
more » ... balance low cost and accuracy in optimal ways. With this framework, we show that several observed behaviors of binary decision-making systems, including random strategies, hysteresis, and irreversibility, are optimal in an information-theoretic sense for various situations. This framework can also be used to quantify the goals around which a decision-making system is optimized and to evaluate the optimality of cellular decision-making systems by a fundamental information-theoretic criterion. As proof of concept, we use the framework to quantify the goals of the externally triggered apoptosis pathway. † Electronic Supplementary Information (ESI) available: Rate distortion function and optimal decision-making strategies computed using a Gaussian distribution of stimuli (Fig. S1 ) and distortion function computed from apoptosis pathway using a Gaussian distribution of stimuli (Fig. S2) . See
doi:10.1039/c2ib90009b pmid:22370552 pmcid:PMC4547352 fatcat:ofjayuiclfdoxjqfqwg2hrb2uq