A theoretical analysis of emergent mechanosensitive phenomena in eukaryotic tissues

细胞如何感知力和刚度，以及这如何影响它们的发育和功能，这一广泛的问题经常导致对细胞机制（包括运动、肌肉生长、细胞分化和对疾病的反应）的成熟化学解释的替代方案。实际上，目前还无法确定这些过程中化学、机械或电子信号的相对重要性。在澄清这些解释方面取得的任何进展，都将是一个很好的起点，可以把生物物理学和机械反应细胞及其组成部分的生物学这两个仍然截然不同的领域结合起来。作为一名BBSRC DTP资助的物理学博士生，我希望至少能朝着这个目标迈出一小步。在博士项目开始时，我们通过检查细胞扩散过程中整合素的聚集来检查细胞运动问题的开始（目前正在等待审核）。通过类似于fak介导的局灶黏附的机械感应，我们研究了在横纹肌肌节中作为机械敏感信号启动器的titin激酶结构域（论文即将完成）。这似乎在运动后的肌节损伤修复和肌肉干细胞分化中起作用（目前正在研究）。我目前正在研究肌肉细胞的比较RNAseq数据集，以确定机械传感在肌肉类型中的相对重要性，以期在肠道细胞中采用相同的方法，并继续探索肠道干细胞的张力诱导分化。我最终希望能够走完整个圈，并利用它来回到伤口愈合中的集体运动开始的问题，以及与实验伙伴合作进行单细胞运动开始的初步工作。在这项工作中，我们迄今为止使用了理论和计算工具的结合，特别是统计场理论，聚合物软物质理论，动力学，n体模拟，动态模拟，转录组学和蛋白质组学大数据分析，我目前正在开发机器学习方面的技能。迄今为止，机械传感领域的大多数实验都是小规模的——不幸的是，这限制了理论分析的范围和有效性。在过去的20年里，原子力显微镜提供了非常好的关于单个机械敏感分子行为的单分子数据，但是关于细胞器或整个细胞的机械敏感反应的知识仍然非常有限。随着更好的RNAseq等数据的出现，对不同化学或物理环境下的细胞器组成进行准确的功能分析似乎只是时间问题。这是一个激动人心的时刻，能够组装所有的工具，将需要拼凑在一起的拼图，即机械敏感细胞的复杂性。我相信，理论和计算方法的结合将为这一努力带来一个不同寻常而有用的角度。

The broad question of how cells sense force and stiffness and how this impacts their development and function often leads to alternatives to well-established chemical explanations to cellular mechanisms, including motion, muscle growth, cell differentiation and the response to disease. Actually determining the relative importance of chemical, mechanical or electrical cues in any of these processes is currently out of reach. Any progress towards squaring these explanations would be a good place to start to bring the still quite distinct fields of biological physics and the biology of mechanically responsive cells and their components together. As a BBSRC DTP funded PhD student in Physics, I hope to make at least some small step towards this goal.We examined the onset of cell motion problem at the start of the PhD project by examining the aggregation of integrins during cell spreading (currently awaiting refereeing). By analogy with FAK-mediated mechanosensing at focal adhesions, we examined the titin kinase domain as a mechanosensitive signalling initiator in striated muscle sarcomeres (paper near completion). This seems to have a role in sarcomere damage repair and muscle stem cell differentiation after exercise (currently examining). I am currently in the process of examining comparative RNAseq datasets for muscle cells to determine the relative importance of mechanosensing in muscle types, with a view to undertake the same approach in gut cells and segue into the exploration of tension-induced differentiation of gut stem cells. I am finally hoping to be able to go full circle and use this to come back to the problem of the collective onset of motion in wound healing, as well as to pursue the initial work on single cell onset of motion in collaboration with experimental partners.In this line of work, we have thus far used a combination of theoretical and computational tools, specifically statistical field theory, polymer soft matter theory, kinetics, N-body simulations, dynamical simulations, transcriptomics and proteomics big data analysis, and I am currently developing skills in machine learning. Most of the experiments to date in mechanosensing have been small scale - unfortunately limiting the scope and effectiveness of a theoretical analysis. Atomic force microscopy has provided very good single molecule data on the behaviour of individual mechanosensing molecules over the last 20 years, but knowledge of the mechanosensitive response of organelles or whole cells is still quite limited. With the advent of ever better RNAseq data and the like, it seems only a matter of time before an accurate functional analysis of the composition of organelles in different chemical or physical environments becomes possible. It is an exciting time to be able to assemble all of the tools which will be required to piece together the puzzle that is the complexity of the mechanosensitive cell. And I believe that a combination of a theoretical and a computational approach will bring an uncommon and useful angle to this endeavour.