Limits and trade-offs of feedback control

 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保持稳定至关重要，就像家庭需要恒温器来维持固定的房间温度一样。在细胞中，反馈被用来调节从基因表达到染色体复制的一切，而反馈的失败会导致一系列人类疾病。但我们对生物学中的反馈的理解是非常不完整的。大多数理论方法使用的数学框架不太适合描述细胞，因为它们忽略了一个主要的扰动来源：化学反应涉及的分子数量较少，而且由于每个单独的反应都是概率的，浓度的波动是自发产生的。与人类设计的系统不同，在人类设计的系统中，控制系统(如恒温器)与它们控制的东西(温度波动)有根本的不同，而电池中的控制系统与它们控制的过程相似。这使得细胞很难抑制扰动，也使得研究人员很难分析控制系统设计。在实验上，很少有系统既能准确测量单细胞中的浓度，又能对控制系统进行系统修改，以分析它们对系统的影响。分析还可能过于集中于一个具体制度的具体细节，以至于一般指导原则被忽视或曲解。我们建议通过开发新的数学方法和系统地将我们的新的实验分析应用于简单的模型系统-细菌质粒来解决这些问题。我们的初步理论表明，负反馈抑制细胞波动的能力受到了硬限制。它还提出了创造性和反直觉的机制，将这些问题降至最低。值得注意的是，我们已经在我们知道处于强选择下以抑制噪音的质粒基因簇中找到了这些例子。除了这些系统对了解反馈控制的有用外，细菌质粒在医学上也非常相关，因为它们导致了医院中的大多数耐药病例，这个问题每年仅在美国就导致数百万严重疾病和数万人死亡。它们也是生物技术行业的关键工具，在生物技术行业，我们研究的波动抑制特性是一个很大的麻烦。质粒无与伦比的实验可操作性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，从而加深对反馈机制的理解，也增加了对质粒行为的有用知识。

 DESCRIPTION (provided by applicant): 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 human 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 human-designed systems, where the control systems (e.g. thermostats) are fundamentally different from what they control (temperature fluctuations), the control systems in cells are similar to the processes they control. This makes it very difficult for cells to suppress perturbations, and also makes it very difficult for researchers to analyze the control system design. 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. 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 simple model systems, 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 enough, we have found examples of these in plasmid gene clusters that we know are under strong selection to suppress noise. In addition to the usefulness of these systems to understand feedback control, bacterial plasmids are also very relevant medically, since they cause the majority of drug 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 for the biotechnology industry, 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.