Limits and Trade-offs of Feedback Control

负反馈控制是使生物系统对内外扰动保持稳定的关键。 它被用来调节从体温到基因表达的一切，它的失败导致了一系列 人类疾病。但我们对生物学中的反馈的理解是不完整和不连贯的。最系统化 理论方法是基于确定性动力学的，不太适合微观细胞事件，并且 很少有系统允许对给定细胞中单个分子的数量进行准确的实验测量。 以前的工作如此密切地关注每个系统的细节，以至于总的指导原则 被忽视，反过来又使详细的分析变得困难。我们建议通过发展 新的数学方法和我们新的实验分子计数分析系统地应用于 简单的模型系统。我们的初步理论证明了负面反馈能力的硬限制 抑制蜂窝系统中的波动，以及在减少一种类型的 相反，变异放大了另一种。它还提出了创造性的机制，将这些问题降至最低，并 值得注意的是，我们现在已经在生物学中找到了这样的例子。我们建议将个人 通过将统计物理和控制理论的中心概念集成到一个新的连贯的 一个框架，使得对强烈的非线性和异国情调做出准确的量化陈述成为可能 系统。该理论还被用于激励、设计和解释两个系统的定量实验 在大肠杆菌中提高毒力和耐药性的物质。我们专注于细菌的复制控制 其中噪声抑制是必不可少的。质粒无与伦比的易操纵性使我们能够 系统地改变控制回路的性质，严格检验一般定理。我们将对这些进行比较 结果与压力响应西格玛系数rpos的结果一致，我们认为负反馈可能会 增强变异性。我们的研究将有助于为生物学中有效的随机性研究奠定基础：它来自哪里， 它是如何被控制的，是什么限制了生物系统如何抑制噪音，以及创造性的方法 生物系统利用这些限制中明显的“漏洞”。我们相信这将暴露出新的原则 这将帮助我们了解生物系统是如何进化的，以及它们如何在健康和疾病中发挥作用。 特别是在促进细菌抗药性和毒力的系统中。

Negative feedback control is essential to make biological systems stable to internal and external perturbations. It is used to regulate everything from body temperature to gene expression, and its failure causes a range of human disorders. But our understanding of feedback in biology is incomplete and incoherent. Most systematic theoretical approaches are based on deterministic kinetics, poorly suited for microscopic cellular events, and few systems allow accurate experimental measurements of the numbers of individual molecules in a given cell. Prior work focused so closely on the details of each system that general guiding principles have been overlooked, in turn making detailed analysis difficult. We propose to address these problems by developing new mathematical approaches and systematically applying our novel experimental molecule counting assays to simple model systems. Our preliminary theory demonstrates hard limits on the ability of negative feedback to suppress fluctuations in cellular systems, and general frustration trade-offs where reducing one type of variation instead amplifies another. It also suggests creative mechanisms that minimize these problems, and remarkably enough, we have now found examples of these in biology. We propose to extend the individual theorems by integrating central concepts from statistical physics and control theory into a novel coherent framework that makes it possible to make exact quantitative statements about strongly nonlinear and exotic systems. The theory is also used to motivate, design and interpret quantitative experiments for two systems that promote virulence and drug resistance in Escherichia coli. We focus on replication control of bacterial plasmids, where noise suppression is essential. The unmatched tractability of plasmids allows us to systematically vary control loop properties and rigorously test the general theorems. We will compare these results with those for stress response sigma factor RpoS, where we believe negative feedback may instead enhance variation. Our studies will help lay the groundwork for effective studies of randomness in biology: where it comes from, how it is controlled, what limits physics sets on how biological systems suppress noise, and the creative ways that biological systems exploit apparent ¿loopholes¿ in those limits. We believe this will expose new principles that will help us understand how biological systems evolved, and how they function in health and disease ¿ particularly in systems that promote drug resistance and virulence in bacteria.