A framework for designing and analyzing binary decision-making strategies in cellular systems.

Joshua R. Porter, Burton W. Andrews, Pablo A. Iglesias

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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 that 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.

Original languageEnglish (US)
Pages (from-to)310-317
Number of pages8
JournalIntegrative biology : quantitative biosciences from nano to macro
Issue number3
StatePublished - Mar 2012

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry


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