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

在本提案中，我将扩展我在快速进化的微生物群体和分化/再生组织中细胞群体动力学建模方面的工作，以制定新的策略，旨在揭示在大量细胞群体中控制集体现象的“统计”规则。我将使用细菌的适应性免疫和细胞重编程作为主要的兴趣系统，但所开发的方法将更普遍适用。在微生物种群的背景下，我将模拟细菌细胞中适应性免疫的集体动力学，这些细胞可能单独无法有效地避免病毒，但总体上种群比其部分做得更好。这项工作将建立在我之前关于适应性免疫的集体特征（PNAS 2018）和限制个体细胞避免病毒能力的生理限制（PNAS 2020）的工作基础上，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的Devaki Bhaya的实验合作为基础。在组织的背景下，我将重点关注细胞重编程，这是一种现象，成熟的，特化的细胞可以被重编程到一个不成熟的状态，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中基因表达的变化，因为它们重编程，以确定重编程过程的关键遗传调节因子。这项工作源于我们最近发现的基因相同细胞重编程能力的巨大差异（《科学》2019）。我们证明了一小部分“精英”细胞对未成熟细胞群的贡献是大多数非精英细胞的数千倍或更多。这项工作将侧重于精英细胞与非精英细胞的“路径统计”，因为它们从成熟状态到不成熟状态遍历高维基因表达空间。该项目的单细胞转录组学数据是由多伦多Lunenfeld-Tanenbaum研究所的Jeff Wrana小组生成的。我的长期目标是揭示在大量细胞中控制结构-功能关系的统计规则。在这里，我将重点扩展我的工作，为大型“强相互作用”的异质细胞群体开发现象学模型，这些细胞群体有效地表现为一个集体。总体而言，我的工作将提供对设计原则的更深入理解，这些原则允许大量细胞从多细胞生物的组织中执行特定功能，以了解复杂的微生物群落如何响应和适应环境变化。

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.