Department of Electrical and Computer Engineering, The Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.
Integr Biol (Camb). 2012 Mar;4(3):310-7. doi: 10.1039/C2IB90009B. Epub 2012 Mar 1.
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.
细胞根据从环境中收集并通过噪声决策途径处理的嘈杂信号做出许多二元（全有或全无）决策。为了提高决策的保真度而降低噪声的影响，这是以增加复杂性为代价的，这在性能和代谢成本之间产生了权衡。我们提出了一个基于率失真理论的框架，这是信息论的一个分支，用于量化这种权衡，并设计出以最佳方式平衡低成本和准确性的二元决策策略。通过这个框架，我们表明，二元决策系统的几种观察到的行为，包括随机策略、滞后和不可逆性，在各种情况下从信息论的意义上讲都是最优的。该框架还可用于量化决策系统优化的目标，并通过基本的信息论标准来评估细胞决策系统的最优性。作为概念验证，我们使用该框架来量化外部触发的细胞凋亡途径的目标。
