From bacteria to mammalian cells: Interrogating single-cell dynamics using agent-based stochastic models

医疗保健面临的最紧迫挑战之一是许多患者对治疗没有反应，这会造成身体，社会和经济上的痛苦。此外，可变的治疗反应有助于药物开发的成本。这些低效率的主要驱动因素是复杂疾病（如癌症）患者内部和患者之间存在的细胞异质性。改变的行为可能只涉及少数细胞，但迄今为止，这种变化通常在群体水平上进行分析，这掩盖了功能和临床相关的细胞间变异。现代单细胞技术允许同时跟踪数百个细胞的生长和细胞内浓度。这些实验的结果很难解释，因为它们在细胞与细胞之间差异很大，即使在相同条件下生长的遗传相同的细胞群中也是如此。需要预测模型来理解这些实验，并了解细胞如何利用这种异质性，例如生存。随着单细胞数据的丰富，解决这个问题将变得至关重要，以解决药物耐受性和疾病的问题，以及改善医疗部门的治疗方法。目前的数学方法量化了细胞中合成分子的反应中固有的随机性，但它们无法预测这些异质性如何影响细胞生长和分裂。为了理解这种效应，我将开发新的数学和模型，使我们能够理解细胞生长和单细胞反应之间的复杂相互作用。这些模型将细胞视为个体，并允许跟踪每个细胞的状态及其在不断增长的细胞群体中的历史。因此，他们提供了一个定量的了解生物数据的实验单细胞分辨率。利用这些数学方法，该项目将揭示细菌和癌细胞群体异质性的原因和后果，这在一系列生物技术和医学应用中具有重要意义。使用理论和实验的结合，我们将解释细胞异质性如何影响基本的细胞功能，如细胞分裂，生长和细胞周期，最终驱动增殖和细胞存活。特别是，我们将研究细胞分裂和细胞周期动力学，以探索异质性如何使细菌科普压力和癌细胞逃避化疗治疗。因此，该项目提出了一个关于细胞异质性在细胞增殖中的作用及其对疾病的影响的变革性和定量的单细胞观点。

One of the most pressing challenges for healthcare is that many patients do not respond to treatment, which produces physical, social, and economic suffering. Moreover, variable treatment response contributes to the cost of drug development. A major driver of these inefficiencies is the cellular heterogeneity existing within and between patients in complex diseases such as cancer. Altered behaviours can involve only a few cells, but to-date such changes are often profiled at the population level, which masks functionally and clinically relevant intercellular variations.Modern single-cell technologies allow tracking growth and intracellular concentrations in hundreds of cells simultaneously. The outcomes of these experiments are difficult to interpret because they vary drastically from cell to cell, even within genetically identical cell populations grown under the same conditions. Predictive models are needed to make sense of these experiments and to understand how cells exploit this heterogeneity, for instance for survival. It will be crucial to address this question with the wealth of single-cell data becoming available to tackle problems of drug tolerance and diseases, as well as to improve therapies for the health sector.Current mathematical approaches quantify the stochasticity inherent in reactions by which molecules are synthesised in the cell, but they cannot predict how these heterogeneous affect cell growth and division. To understand this effect, I will develop new mathematics and models that enable us to understand the complex interplay between cell growth and the reactions in single cells. These models treat cells as individuals and allow tracking the state of every cell and their histories in a growing cell population. They thus provide a quantitative understanding of biological data at the experimental single-cell resolution. Using these mathematical methods, the project will uncover the causes and consequences of heterogeneity in bacterial and cancer cell populations, which has important implications in a range of biotechnological and medical applications. Using a combination of theory and experiment, we will explain how cellular heterogeneity affects essential cellular functions such as cell division, growth and the cell cycle that ultimately drive proliferation and cell survival. In particular, we will investigate cell division and cell cycle kinetics to explore how heterogeneity allows bacteria to cope with stress and cancer cells to evade chemotherapeutic treatment. The project thus presents a transformative and quantitative single-cell perspective on the role of cellular heterogeneity in cell proliferation and its implications for disease.