蜂窝信令网络作为广义Wiener-Kolmogorov滤波器来抑制噪声
Cellular signaling networks function as generalized Wiener-Kolmogorov filters to suppress noise
摘要：
细胞信号传导涉及通过随机生化反应的级联来传递环境信息，不可避免地引入损害信号保真度的噪声。级联反应的每个阶段通常以激酶-磷酸酶推拉网络的形式出现，这是信号通路的基本单位，其功能障碍与许多癌症有关。我们发现这种无处不在的酶网络模体有效地表现为维纳-柯尔莫哥洛夫（WK）最优噪声滤波器。使用概念的影演算，我们推广的线性WK理论，最初介绍了在通信和控制工程的背景下，考虑到非线性信号转导和离散分子群体。这使我们能够得到严格的约束，有效地降低噪音，在这个生化系统。我们的数学形式主义产生的情况下，重要的细胞功能的过滤器性能的界限-像超敏感的刺激反应。我们强调了与优化过滤器效率相关的系统功能，编码在一个单一的，可测量的，无量纲的参数中。我们的理论，它描述了在一个大类的信号转导网络的噪声控制，也是有用的合成生化信号通路的设计，并通过实验探针，如振荡输入的路径的操作。
英文摘要：
Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov (WK) optimal noise filter. Using concepts from umbral calculus, we generalize the linear WK theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function---like ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways, and the manipulation of pathways through experimental probes like oscillatory input.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
