Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Phys Biol. 2012 Aug;9(4):045011. doi: 10.1088/1478-3975/9/4/045011. Epub 2012 Aug 7.
Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell's signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon's information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.
细胞信号可以被认为从根本上是一个信息传递问题，其中化学信使将关于外部环境的信息中继到细胞内的决策中心。由于细胞信号转导网络的生化性质，分子噪声不可避免地会限制细胞信号转导网络接收到和处理的任何信息的保真度，使细胞对其环境的印象不完美。幸运的是，香农信息论提供了一个独立于网络复杂性的数学框架，可以量化可以传输的信息量，尽管存在生化噪声。具体来说，信道容量可用于测量细胞能够区分的最大刺激数量，这是基于其信号系统的噪声响应。在这里，我们为定量生物学家提供了一个入门指南，涵盖了信息论的基本概念，强调了在实验测量信道容量时需要考虑的几个关键因素，并描述了信息论分析在生物信号中的应用的成功示例。
