Limits and Trade-offs of Feedback Control

反馈控制的限制和权衡

• 批准号: 8232018
• 负责人: Johan Paulsson
• 金额: $ 33.23万
• 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 2014-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8232018
• 关键词: Address; Bacteria; Bacteria sigma factor KatF protein; Biochemical; Biological; Biological Assay; Biological Models; Biological Process; Biology; Body Temperature; Cells; Complex; Control Groups; Disadvantaged; Disease; Drug resistance; Engineering; Escherichia coli; Event; Extinction (Psychology); Failure; Feedback; Frustration; Gene Expression; Goals; Growth; 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

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.

负反馈控制对于使生物系统对内部和外部扰动保持稳定至关重要。 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的疾病。 人类疾病但我们对生物学中反馈的理解是不完整和不连贯的。最系统 理论方法基于确定性动力学，不适合微观细胞事件， 很少有系统允许对给定细胞中的单个分子的数量进行精确的实验测量。 以前的工作如此密切地关注每个系统的细节， 这一点被忽视了，反过来又使详细的分析变得困难。我们建议通过发展 新的数学方法和系统地应用我们的新实验分子计数测定， 简单的模型系统我们的初步理论表明，负反馈的能力有很大的限制， 抑制蜂窝系统中的波动，以及减少一种类型的 变异反而放大了另一个变异。它还建议创造性的机制，尽量减少这些问题， 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 定理整合中心概念从统计物理学和控制理论到一个新的连贯 框架，使之有可能作出精确的定量陈述强非线性和异国情调 系统.该理论也被用于激励、设计和解释两个系统的定量实验 促进大肠杆菌的毒力和耐药性。我们专注于细菌的复制控制 质粒，其中噪声抑制是必不可少的。质粒无与伦比的易处理性使我们能够 系统地改变控制回路的性质，并严格测试一般定理。我们将比较这些 结果与那些压力反应西格玛因素RpoS，我们认为负反馈，而不是 增强变异。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：它来自哪里， 它是如何被控制的，物理学对生物系统如何抑制噪音的限制是什么，以及创造性的方法 生物系统利用了这些限制中明显的漏洞。我们相信这将揭示新的原则 这将有助于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。