Limits and Trade-offs of Feedback Control

反馈控制的限制和权衡

• 批准号: 7597111
• 负责人: Johan Paulsson
• 金额: $ 33.88万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-04-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7597111
• 关键词: Address; Bacteria; Bacteria sigma factor KatF protein; Biochemical; Biological; Biological Assay; Biological Models; Biological Process; Biology; Body Temperature; Cells; Classification; Complex; Control Groups; Disadvantaged; Disease; Drug resistance; Engineering; Escherichia coli; Event; Extinction (Psychology); Failure; Feedback; Frustration; Gene Expression; Goals; Growth; Half-Life; Health; Homeostasis; Human; Individual; Kinetics; Laws; Literature; Measurement; Measures; Memory; Methods; Microscopic; Modeling; Molecular; Noise; Phase; Physics; Plasmids; Probability Theory; Process; Property; Proteins; Sigma Factor; Source; Stress; System; Testing; Time; Variant; Virulence; Work; base; biological adaptation to stress; biological systems; design; improved; inhibitor/antagonist; novel; promoter; research study; response; simulation; theories; tool

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.

描述（由申请人提供）：负反馈控制对于使生物系统对内部和外部扰动稳定至关重要。它被用来调节从体温到基因表达的一切，它的失败会导致一系列人类疾病。但我们对生物学反馈的理解是不完整且不连贯的。大多数系统理论方法都是基于确定性动力学，不太适合微观细胞事件，而且很少有系统能够对给定细胞中单个分子的数量进行准确的实验测量。先前的工作过于关注每个系统的细节，以至于忽略了一般指导原则，从而使详细分析变得困难。我们建议通过开发新的数学方法并将我们的新颖的实验分子计数测定方法系统地应用于简单的模型系统来解决这些问题。我们的初步理论表明，负反馈抑制细胞系统波动的能力存在严格限制，以及一般的挫败权衡，即减少一种变化反而放大另一种变化。它还提出了最大限度地减少这些问题的创造性机制，而且值得注意的是，我们现在已经在生物学中找到了这些例子。我们建议通过将统计物理学和控制理论的中心概念整合到一个新颖的连贯框架中来扩展各个定理，该框架使得对强非线性和奇异系统做出精确的定量陈述成为可能。该理论还用于激发、设计和解释两个促进大肠杆菌毒力和耐药性的系统的定量实验。我们专注于细菌质粒的复制控制，其中噪声抑制至关重要。质粒无与伦比的易处理性使我们能够系统地改变控制环特性并严格测试一般定理。我们将这些结果与压力反应西格玛因子 RpoS 的结果进行比较，我们认为负反馈可能反而会增强变异。公共健康相关性：我们的研究将有助于为生物学中随机性的有效研究奠定基础：它从哪里来，如何控制，什么限制了生物系统如何抑制噪音的物理设置，以及生物系统利用这些限制中明显漏洞的创造性方式。我们相信，这将揭示新的原理，帮助我们了解生物系统如何进化，以及它们如何在健康和疾病中发挥作用，特别是在促进细菌耐药性和毒力的系统中。