Understanding the population structure and fate of large rapidly evolving populations

了解人口结构和大量快速进化人口的命运

• 批准号: RGPIN-2015-05241
• 负责人: Goyal, Sidhartha
• 金额: $ 1.6万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=678922
• 关键词: Understanding; population; structure; fate; large

Much of complex biology results from interactions among a large number of individually simple elements. For example, the composition of the large interacting population of microbes that coexist in an animal's mouth and gut profoundly affect its physiology. Similarly, about 1011 new blood cells regenerate everyday from a much smaller yet a large population of epigenetically and developmentally diverse stem cell population. Thus understanding the general principles that govern the collective dynamics of large population of cells is vital for a deeper understanding of a variety of biological phenomena.***Much like thermodynamic laws that capture the average macroscopic behavior of large number of molecules, the goal of this program is to build coarse-grained models that capture the dynamics of key observable features of large populations in a few specific experimental and computational systems. Focusing on specific systems will allow us to develop these models in a systematic manner, in continuous validation by experiments. Specific systems considered in this work are: (1) collective dynamics of bacteria and its predatory viruses (phages), (2) role of intracellular selection in maintaining mitochondrial DNA fidelity, and (3) understand temporal stability of blood regeneration in animals.***Collective dynamics of large populations of cells is fueled by cell-to-cell variability resulting from a combination of genetic and epigenetic mechanisms. What variations spread in the population depends on a combination of physical processes (stochastic dynamics and spatiotemporal interactions among cells) and the effect of variation on cellular phenotype. The laws that describe the spread of variation are not well understood. This is because the dynamics is very nonlinear and stochastic. To make progress we utilize deep sequencing to directly track individual trajectories of genomic variations as they arise and spread (or go extinct) in the laboratory experiments. Armed with these trajectories we then develop a statistical theory for relevant macroscopic properties using the tools from Statistical/Computational Physics.***Trainees who will be funded through this proposal will receive multi-faceted training in statistical physics, bioinformatics, evolutionary dynamics, high-performance scientific computing, and systems biology, making them skilled candidates for either academia or biomedical industries. These skills will help meet the current and future demand for a highly qualified workforce in Canada.**

许多复杂的生物学是由大量单独的简单元素之间的相互作用产生的。例如，共存于动物口腔和肠道中的大量相互作用的微生物群的组成深刻地影响其生理机能。同样，每天大约有 1011 个新血细胞从数量少得多但数量大的表观遗传和发育多样性干细胞群中再生。因此，了解控制大量细胞集体动力学的一般原理对于更深入地了解各种生物现象至关重要。***就像捕捉大量分子的平均宏观行为的热力学定律一样，该程序的目标是建立粗粒度模型，以捕捉少数特定实验和计算系统中大量细胞的关键可观察特征的动态。专注于特定系统将使我们能够系统地开发这些模型，并通过实验不断验证。这项工作考虑的具体系统是：(1) 细菌及其捕食性病毒（噬菌体）的集体动力学，(2) 细胞内选择在维持线粒体 DNA 保真度中的作用，以及 (3) 了解动物血液再生的时间稳定性。***大量细胞的集体动力学是由遗传和表观遗传机制组合产生的细胞间变异性推动的。群体中传播的变异取决于物理过程（细胞间的随机动力学和时空相互作用）以及变异对细胞表型的影响的组合。描述变异传播的规律尚不清楚。这是因为动力学是非常非线性和随机的。为了取得进展，我们利用深度测序来直接跟踪基因组变异在实验室实验中出现和传播（或灭绝）的个体轨迹。有了这些轨迹，我们就可以利用统计/计算物理学的工具，开发出相关宏观特性的统计理论。***通过本提案资助的学员将接受统计物理学、生物信息学、进化动力学、高性能科学计算和系统生物学方面的多方面培训，使他们成为学术界或生物医学行业的熟练候选人。这些技能将有助于满足加拿大当前和未来对高素质劳动力的需求。**