Limits and trade-offs of feedback control

反馈控制的限制和权衡

• 批准号: 10735383
• 负责人: Johan Paulsson
• 金额: $ 46.2万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-07-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10735383
• 关键词: Address; Affect; Bacteria; Bacteriophage lambda; Behavior; Binding; Biological Assay; Biological Models; Biological Process; Biology; Biotechnology; Cell Cycle; Cell Separation; Cell division; Cells; Cessation of life; Chemicals; Complex; Concentration measurement; Creativeness; Disease; Disease Outbreaks; Event; Failure; Feedback; Gene Expression; Generations; Genotype; Goals; Grant; Growth; Health; Home; Horizontal Gene Transfer; Hospitals; Human; Human body; Image; Individual; Kinetics; Knowledge; Lac Operon; Libraries; Maintenance; Maps; Mathematics; Measures; Medical; Methods; Microscopy; Modeling; Modification; Molecular; Molecular Biology; Molecular Machines; Multi-Drug Resistance; Mutation; Noise; Pattern; Pharmaceutical Preparations; Phenotype; Plasmid Cloning Vector; Plasmids; Population; Process; Property; Randomized; Reaction; Reporter; Research Personnel; Resolution; Source; Stochastic Processes; System; Temperature; Testing; Time; Visualization; biological systems; chemical reaction; chromosome replication; cost; experimental study; insight; life history; man; mathematical methods; mathematical theory; mutant; novel; replicator; segregation; single molecule; theories; tool

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.

项目摘要 负反馈控制对于使生物系统稳定在内部和外部扰动中至关重要， 就像房屋需要恒温器以保持固定的室温一样。在单元格中，反馈用于调节 从基因表达到染色体复制，其失败都会引起各种疾病。但是我们的 了解生物学中的反馈是非常不完整的。大多数理论方法都使用数学 框架不适合描述细胞，因为它们忽略了扰动的主要来源：化学 反应涉及数量少的分子，并且由于每个反应都是概率的，因此在 浓度自发出现。与人造机器不同，控制系统（例如恒温器） 与它们控制的过程（温度波动）根本不同，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 扰动，也使研究人员很难分析控制。实验，很少的系统 允许对单个细胞中的浓度进行准确的测量以及对照的系统修饰 分析它们如何影响系统的系统。个人分析也可以紧密关注特定 一般指导原则被忽略或误解的一个特定系统的详细信息。 我们建议通过开发新的数学方法并系统地解决这些问题 将我们的新实验测定法应用于中央模型系统：细菌质粒。我们的初步理论 对负反馈抑制细胞波动的能力表明了硬限制。这也暗示了 创造性和违反直觉的机制，使这些问题最小化。值得注意的是，我们发现了例子 在我们知道的质粒中，其中这些是强烈选择以抑制噪声的。我们还开发了三个 实验平台的类型，用于定量解决细胞的波动和控制 特别是：准确且独立验证的单分子计数测定法，高通量 细胞跟踪平台使我们能够使用显微镜遵循十亿个单元格，并获得高度准确的测定 一百万平行谱系和控制突变体的基因型到表型映射。 除了从根本上理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，因为它们会导致大多数多药抗性病例 医院，这是一个问题，每年导致数百万个严重疾病和成千上万的死亡 我们一个人。它们也是生物技术的关键工具，我们研究的波动抑制属性是 一个重要的滋扰。质粒的无与伦比的实验障碍性使我们能够系统地改变 控制属性并严格地测试数学描述，从而导致更深层次 了解反馈机制，并增加有关质粒行为的有用知识。

项目成果

The processive kinetics of gene conversion in bacteria.

• DOI: 10.1111/mmi.13661
• 发表时间: 2017-06
• 期刊: Molecular microbiology
• 影响因子: 3.6
• 作者: Paulsson J;El Karoui M;Lindell M;Hughes D
• 通讯作者: Hughes D

Fundamental limits on the suppression of molecular fluctuations.

• DOI: 10.1038/nature09333
• 发表时间: 2010-09-09
• 期刊: Nature
• 影响因子: 64.8

Constraints on Fluctuations in Sparsely Characterized Biological Systems.

• DOI: 10.1103/physrevlett.116.058101
• 发表时间: 2016-02-05
• 期刊: Physical review letters
• 影响因子: 8.6
• 作者: Hilfinger A;Norman TM;Vinnicombe G;Paulsson J
• 通讯作者: Paulsson J

Mechanical slowing-down of cytoplasmic diffusion allows in vivo counting of proteins in individual cells.

• DOI: 10.1038/ncomms11641
• 发表时间: 2016-05-18
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Okumus B;Landgraf D;Lai GC;Bakshi S;Arias-Castro JC;Yildiz S;Huh D;Fernandez-Lopez R;Peterson CN;Toprak E;El Karoui M;Paulsson J
• 通讯作者: Paulsson J

Non-genetic heterogeneity from stochastic partitioning at cell division.

• DOI: 10.1038/ng.729
• 发表时间: 2011-02
• 期刊: Nature genetics
• 影响因子: 30.8