Phenomenological models of large complex populations of cells: from microbes to tissues

大型复杂细胞群的现象学模型：从微生物到组织

• 批准号: RGPIN-2021-03731
• 负责人: Goyal, Sidhartha
• 金额: $ 2.48万
• 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: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=762697
• 关键词: Phenomenological; models; large; complex; populations

In this proposal, I will extend my work on modeling population dynamics of cells in rapidly evolving microbial populations and differentiating/regenerating tissues to develop new strategies aiming at uncovering "statistical" rules governing collective phenomena in large populations of cells. I will use adaptive immunity in bacteria and cellular reprogramming as primary systems of interest, but the methods developed will be more generally applicable. In the context of microbial populations, I will model collective dynamics of adaptive immunity in bacterial cells that may individually not be efficient in avoiding viruses but collectively the population does better than its parts. This work will build upon my previous work on collective features of adaptive immunity (PNAS 2018) and the physiological constraints that limit individual cell's ability to avoid viruses (PNAS 2020), with a broader focus on how viruses affect microbial diversity and their fate. This project will be grounded by present experimental collaborations with Devaki Bhaya at Stanford University. In the context of tissues, I will focus on cellular reprogramming, which is a phenomenon where mature, specialized cells can be reprogrammed to an immature state capable of developing into all tissues of the body. Here I will track and analyze changes in gene expression in individual cells as they reprogram to identify key genetic regulators of the reprogramming process. This work stems from the large variability we recently discovered (Science 2019) in reprogramming ability amongst genetically identical cells. We demonstrated that a small fraction of "elite" cells contributes thousand-fold or more to the immature cell population than do most non-elite cells. This work will focus on the "path statistics" of elite versus non-elite cells as they traverse the high dimensional gene-expression space from a mature to an immature state. The single cell transcriptomics data for this project is being generated with Jeff Wrana's group at the Lunenfeld-Tanenbaum Research Institute in Toronto. My long-term ambition is to uncover the statistical rules that govern the structure-function relationship in large population of cells. Here, I will focus on extending my work on developing phenomenological models for large "strongly interacting" heterogeneous population of cells that effectively behave as a collective. Overall, my work will provide a deeper understanding of design principles that allow large collection of cells to perform specific functions from tissues in multicellular organisms, to understanding how complex microbial communities respond and adapt to environmental changes.

在这项提案中，我将扩展我在快速进化的微生物种群中对细胞群体动力学进行建模以及组织分化/再生方面的工作，以开发新的战略，旨在揭示管理大量细胞群体集体现象的“统计学”规则。我将使用细菌的适应性免疫和细胞重新编程作为主要的感兴趣的系统，但所开发的方法将更普遍地适用。在微生物群体的背景下，我将对细菌细胞中适应性免疫的集体动态进行建模，这些群体在单独避免病毒方面可能不是有效的，但总体上来说，群体比其部分表现得更好。这项工作将建立在我之前关于适应性免疫的集体特征的工作(PNAS 2018)和限制单个细胞避免病毒能力的生理限制(PNAS 2020)的基础上，更广泛地关注病毒如何影响微生物多样性及其命运。该项目将以目前与斯坦福大学Devaki Bhaya的实验合作为基础。在组织的背景下，我将重点介绍细胞重新编程，这是一种成熟的、专门的细胞可以重新编程到不成熟状态的现象，能够发育成身体的所有组织。在这里，我将跟踪和分析单个细胞在重新编程时基因表达的变化，以确定重新编程过程的关键遗传调节因素。这项工作源于我们最近在遗传相同的细胞之间重新编程能力方面发现的巨大差异(《科学2019》)。我们证明，与大多数非精英细胞相比，一小部分“精英”细胞对未成熟细胞群体的贡献是数千倍或更多。这项工作将集中在精英细胞与非精英细胞在高维基因表达空间中从成熟状态到未成熟状态的“路径统计”上。该项目的单细胞转录组数据是由多伦多伦恩菲尔德-塔南鲍姆研究所的Jeff Wrana团队生成的。我的长期抱负是揭示管理大量细胞群体结构-功能关系的统计规则。在这里，我将专注于扩展我的工作，为有效地作为一个集体行为的大型“强相互作用”的异质细胞群体开发现象学模型。总体而言，我的工作将提供对设计原则的更深层次的理解，这些设计原则允许大量细胞收集来执行多细胞生物体中组织的特定功能，并理解复杂的微生物群落如何响应和适应环境变化。