A theoretical analysis of emergent mechanosensitive phenomena in eukaryotic tissues

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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.