Multicellular dynamics and evolution in dense populations

细胞进化虽然是生命最基本的概念之一，但也是现代医疗中一些最紧迫的挑战的根本原因，如肿瘤发生和耐药性的进化。然而，迄今为止最有针对性的进化实验都是在稀释微生物种群或二维菌落中进行的。这意味着我们对三维实体肿瘤中组成细胞之间的内在机械相互作用如何塑造关键的进化驱动力知之甚少，例如自然选择和遗传漂移。为了应对这些挑战，我提出了一种多学科的方法，将来自物理学、种群遗传学和癌症生物学的概念与创新的遗传工具和复杂的细胞模型系统相结合，以彻底改变我们如何在密集细胞群体中研究进化。利用一系列互补的微生物和癌细胞分析，我将研究(I)集体多细胞动力学如何影响遗传多样性和耐药性进化，(Ii)3D集体细胞运动如何影响进化和治疗反应，以及(Iii)细胞的机械属性及其对外力的敏感性如何重塑自然选择和癌症肿瘤中的遗传漂移。这项提议的完成将建立一个新的框架，将细胞进化描述为活跃颗粒物质中的一种紧急现象，为实验进化领域未来的科学目标打开大门。最后，将所获得的见解应用于生物医学将对更好地了解肿瘤进展和耐药性进化至关重要，同时为改进癌症治疗的基于进化的创新治疗策略提供跳板。

Cellular evolution, while being one of the most fundamental concepts of life, is also the root cause for some of the most pressing challenges in modern health care, such as oncogenesis and the evolution of drug resistance.Yet, most pertinent evolution experiments to date have been conducted in dilute microbial populations or 2-dimensional colonies. This means we know little about how the immanent mechanical interactions between the constituent cells in 3-dimensional solid tumors shape key evolutionary driving forces, such as natural selection and genetic drift.To address these challenges, I propose a multidisciplinary approach that will combine concepts from physics, population genetics and cancer biology with innovative genetic tools and sophisticated cellular model systems to revolutionize how we study evolution in dense cellular populations. Using a series of complementary microbial and cancer cell assays, I will investigate (i) how collective multicellular dynamics impact genetic diversity and drug resistance evolution, (ii) how collective cell motion in 3D affect evolution and treatment response, and (iii) how the mechanical properties of cells and their susceptibility to external forces reshape natural selection and genetic drift in cancer tumoroids.The completion of this proposal will establish a new framework describing cellular evolution as an emergent phenomenon in active granular matter, opening the door to future scientific objectives in the field of experimental evolution. Finally, biomedical application of the insights gained will be essential to better understanding tumor progression and drug resistance evolution while providing a springboard for innovative evolution-based treatment strategies for improved cancer therapy.