A theoretical analysis of emergent mechanosensitive phenomena in eukaryotic tissues

细胞如何感知力量和僵硬，以及这如何影响它们的发育和功能，这一广泛的问题往往会导致对细胞机制的成熟化学解释的替代，包括运动、肌肉生长、细胞分化和对疾病的反应。要真正确定化学、机械或电子信号在这些过程中的相对重要性，目前还无法实现。任何针对这些解释的进展都将是一个很好的起点，可以开始将生物物理学和机械响应细胞及其组件的生物学仍然截然不同的领域结合在一起。作为BBSRC DTP资助的物理学博士生，我希望至少朝着这个目标迈出一些小的一步。我们在博士项目开始时通过检查细胞扩散过程中整合素的聚集(目前正在等待裁判)来检查细胞运动问题的开始。通过与FAK介导的局灶性粘连机械感觉类似，我们研究了肌动蛋白激酶结构域在横纹肌肌瘤中作为机械敏感的信号起始器(论文即将完成)。这似乎在运动后肌节损伤修复和肌肉干细胞分化中起到了作用(目前正在检测)。我目前正在研究肌肉细胞的比较RNAseq数据集，以确定机械感觉在肌肉类型中的相对重要性，以期在肠道细胞中采用同样的方法，并开始探索张力诱导肠道干细胞的分化。我终于希望能够回到创伤愈合中集体运动开始的问题上来，并与实验伙伴合作继续进行单细胞开始运动的初步工作。在这项工作中，到目前为止，我们使用了理论和计算工具的组合，特别是统计场理论、聚合物软物质理论、动力学、N体模拟、动力学模拟、转录学和蛋白质组学大数据分析，目前我正在发展机器学习方面的技能。到目前为止，机械传感方面的大多数实验都是小规模的--不幸的是，限制了理论分析的范围和有效性。在过去的20年里，原子力显微镜已经提供了关于单个机械传感分子行为的非常好的单分子数据，但关于细胞器或整个细胞的机械敏感响应的知识仍然相当有限。随着越来越好的RNAseq数据和类似数据的出现，对不同化学或物理环境中细胞器的组成进行准确的功能分析似乎只是个时间问题。这是一个令人兴奋的时刻，能够组装所有需要拼凑谜团的工具，这就是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和有用的角度。

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.