Limits and trade-offs of feedback control

项目总结 负反馈控制对于使生物系统对内部和外部扰动稳定是必不可少的， 就像家庭需要恒温器来维持固定的室温一样。在细胞中，反馈被用来调节 从基因表达到染色体复制，它的失败会导致一系列疾病。但我们的 对生物学中的反馈的理解是非常不完整的。大多数理论方法都使用数学方法 不适合描述细胞的框架，因为它们忽略了扰动的一个主要来源：化学 反应涉及的分子数很少，而且由于每个单独的反应都是概率的，所以 浓度是自发产生的。与人造机器不同，在人造机器中，控制系统(如恒温器) 从根本上不同于它们控制的过程(温度波动)，化学控制 细胞中的系统类似于它们控制的化学过程。这使得细胞很难抑制 摄动，也使研究人员难以分析控制。从实验上讲，很少有系统 允许对单个细胞中的浓度进行准确测量，也允许对对照进行系统修改 系统来分析它们如何影响系统。单独的分析也可以如此紧密地集中在特定的 总的指导原则被忽视或误解的一个具体制度的细节。 我们建议通过开发新的数学方法和系统地解决这些问题 将我们的新实验分析方法应用于中心模型系统：细菌质粒。我们的初步理论 演示了对抑制细胞波动的负反馈能力的硬限制。它还表明， 创造性和反直觉的机制，最大限度地减少这些问题。值得注意的是，我们已经找到了一些例子 其中，我们所知道的那些处于强烈选择下的质粒中的这些可以抑制噪音。我们还开发了三个 用于定量处理细胞中的波动和控制的实验平台类型 尤其是质粒：准确和独立验证的单分子计数分析，高通量 细胞跟踪平台允许我们用显微镜跟踪十亿个细胞分裂，并进行高精度的分析 百万个平行谱系和对照突变体的基因-表型映射。 除了从根本上了解波动和反馈控制的重要性外，细菌 质粒在医学上也非常相关，因为它们导致了大多数多药耐药病例 医院，这个问题每年导致数以百万计的严重疾病和数万人死亡 只有我们两个。它们也是生物技术中的关键工具，我们研究的波动抑制特性是 一个很大的麻烦。无与伦比的实验处理能力使我们能够系统地改变 控制属性并通过实验严格测试数学描述，从而导致更深层次的 对反馈机制的理解，以及关于质粒行为的有用知识的增加。

PROJECT SUMMARY Negative feedback control is essential to make biological systems stable to internal and external perturbations, just as homes need thermostats to maintain a fixed room temperature. In cells feedback is used to regulate everything from gene expression to chromosome replication, and its failure causes a range of disorders. But our understanding of feedback in biology is very incomplete. Most theoretical approaches use mathematical frameworks that are poorly suited to describe cells because they ignore a main source of perturbations: chemical reactions involve molecules in low numbers and since each individual reaction is probabilistic, fluctuations in concentrations arise spontaneously. Unlike man-made machines, where the control systems (e.g. thermostats) are fundamentally different from the processes they control (temperature fluctuations), the chemical control systems in cells are similar to the chemical processes they control. This makes it difficult for cells to suppress perturbations, and also makes it difficult for researchers to analyze control. Experimentally, very few systems allow both accurate measurements of concentrations in single cells and systematic modifications of the control system to analyze how they affect the system. Individual analyses can also focus so closely on the specific details of one specific system that general guiding principles are overlooked or misinterpreted. We propose to address these problems by developing new mathematical approaches and systematically applying our novel experimental assays to central model system: bacterial plasmids. Our preliminary theory demonstrates hard limits on the ability of negative feedback to suppress fluctuations in cells. It also suggests creative and counter-intuitive mechanisms that minimize these problems. Remarkably, we have found examples of these in plasmids that we know are under strong selection to suppress noise. We have also developed three types of experimental platforms to quantitatively address fluctuations and control in cells in general and for plasmids in particular: Accurate and independently validated single-molecule counting assays, high-throughput cell tracking platforms allowing us to follow a billion cell divisions with microscopy, and assays for highly accurate genotype-to-phenotype mapping for a million parallel lineages and control mutants. In addition to the importance of fundamentally understanding fluctuations and feedback control, bacterial plasmids are also very relevant medically, since they cause the majority of multidrug resistance cases in hospitals, a problem that leads to millions of serious illnesses and tens of thousands of deaths annually in the US alone. They are also key tools in biotechnology, where the fluctuation suppression properties we study are a significant nuisance. The unmatched experimental tractability of plasmids allows us to systematically vary control properties and rigorously test the mathematical descriptions experimentally, leading to a deeper understanding of feedback mechanisms and also an increase in useful knowledge about plasmid behavior.