NSF/PHY-BSF: Statistical Physics of Control and Evolution in Cellular Networks

NSF/PHY-BSF：蜂窝网络中控制和演化的统计物理

• 批准号: 1707961
• 负责人: Elijah Roberts
• 金额: $ 75万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707961&HistoricalAwards=false
• 关键词: NSF; PHY; BSF; Statistical; Physics

Numerous cellular processes do not follow deterministic rules; i.e., genetically identical cells can display different phenotypes even in identical environments. Such processes involve cellular decision making, in which individual cells probabilistically make choices determining their fate. One view is that the probabilistic nature of cellular decision-making originates from stochastic noise present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise of these networks. Yet extrinsic or environmental noise may be much more significant, likely governing the overall dynamics. The goal of this research project is to develop a concise theoretical framework describing the combined effect of intrinsic and extrinsic noise on the stochastic dynamics of genetic switches responsible for cellular decision-making. The PI hypothesizes that extrinsic noise not only significantly lowers the escape time from a metastable decision state, but can fundamentally change the actual escape mechanism. To elucidate the role of environmental noise on genetic circuits, the project will investigate, numerically and analytically, the interplay between intrinsic and extrinsic noise in a series of increasingly complex models of cellular decision processes. Noise-driven switching between states plays a key role in various phenomena including chemical reactions, nanomagnets, Josephson junctions, genetic switches, and protein folding. The methods investigated here can be used to study a range of physical problems. All code developed during the research activities of the project will be made available as open source software. Software protocols will be developed to ensure that all software can be easily used by other research groups. Also, all simulation and experimental datasets produced by the project will be made available for download as an "Open Science" policy, to encourage reproduction and extension of results. All of the educational curriculum will also be publicly released for other educators to use and adapt for their own courses.A sophisticated model for the contribution of extrinsic noise to cellular decision-making will be critical toward developing computational approaches to advanced synthetic biology. To develop artificial genetic programs that are as sophisticated as natural developmental ones will require a concerted effort to understand the basic physical principles by which decision-making networks operate. This project will build underlying knowledge in three specific areas. 1) The fundamental building blocks of decision making networks. 2) The response of networks to external fluctuations. 3) The impact of fluctuations on the evolution of microbial organisms. The educational component of this proposal will improve quantitative training for future genetic designers. The PI teaches a biophysically oriented systems biology undergraduate course, attempting to integrate physics-based modeling with data-driven modeling. The PI also teaches a graduate quantitative biology course for biology students. New regulatory design projects will be incorporated into both courses by creating a computational version of the BioBricks library that students can use to model regulatory designs, using the research software. The PI will also institute an outreach program for underrepresented and economically disadvantaged high-school students to learn about physics, biology, and computers using a programming "game" based on the research in the proposal.This project is being jointly supported by the Physics of Living Systems program in the Division of Physics and the Systems and Synthetic Biology Cluster in the Division of Molecular and Cellular Biosciences.

许多细胞过程不遵循确定性规则;即，即使在相同的环境中，遗传上相同的细胞也可以表现出不同的表型。这些过程涉及细胞决策，其中单个细胞概率性地做出决定其命运的选择。一种观点认为，细胞决策的概率性质源于生物分子相互作用网络中存在的随机噪声。以前的大多数工作都集中在这些网络的固有噪声。然而，外部或环境噪声可能更重要，可能会控制整体动态。本研究项目的目标是开发一个简洁的理论框架，描述内在和外在噪声对负责细胞决策的遗传开关的随机动力学的综合影响。PI假设外部噪声不仅显着降低了从亚稳态决策状态的逃逸时间，而且可以从根本上改变实际的逃逸机制。为了阐明环境噪声对遗传电路的作用，该项目将在一系列日益复杂的细胞决策过程模型中，从数值和分析上研究内在和外在噪声之间的相互作用。噪声驱动的状态之间的切换在各种现象中起着关键作用，包括化学反应，纳米磁体，约瑟夫森结，遗传开关和蛋白质折叠。这里研究的方法可以用来研究一系列物理问题。项目研究活动期间开发的所有代码将作为开放源码软件提供。将制定软件协议，以确保所有软件都能方便其他研究小组使用。此外，该项目产生的所有模拟和实验数据集将作为“开放科学”政策提供下载，以鼓励复制和推广结果。所有的教育课程也将公开发布，供其他教育工作者使用和适应自己的课程。外部噪声对细胞决策的贡献的复杂模型对于开发先进合成生物学的计算方法至关重要。要开发出与自然发育程序一样复杂的人工遗传程序，就需要共同努力，理解决策网络运作的基本物理原理。该项目将在三个具体领域积累基本知识。1)决策网络的基本组成部分。2)网络对外部波动的响应。3)波动对微生物进化的影响。这项建议的教育部分将改善未来基因设计师的定量培训。PI教授一门以生物制药学为导向的系统生物学本科课程，试图将基于物理的建模与数据驱动的建模相结合。PI还为生物学学生教授研究生定量生物学课程。新的监管设计项目将通过创建BioBricks库的计算版本纳入这两门课程，学生可以使用研究软件来模拟监管设计。PI还将制定一项针对代表性不足和经济困难的高中生的推广计划，以该计划中的研究为基础，通过编程“游戏”学习物理学、生物学和计算机。该项目由物理学部门的生命系统物理学计划和分子与细胞生物科学部门的系统与合成生物学集群共同支持。