| dc.description.abstract | Spatial architecture in biological systems, such as human tissues, plays a crucial role in how they evolve and function over time. Despite longitudinal sampling in humans often being infeasible, inhibiting direct measurement of past evolution, earlier dynamics leave a footprint on the spatial arrangement of cells. The link between evolution and the resulting spatial architecture remains largely unknown, however, yet understanding this relationship could help to elucidate past tissue dynamics, including early cancer evolution. In this thesis, we explore the spatial signatures of somatic evolution in normal and cancerous human tissues, investigating how to utilise spatial patterns of DNA mutations to study past evolution. We introduce two new methodologies for spatial analysis of tumour tissue, exploiting random walk theory and fractal geometry to quantify patterns of tumour sub-population mixing. Combining analysis of experimentally measured colorectal cancer with spatial agent-based modelling, we establish frameworks to quantify the replicative advantage, time of emergence, and pushing strength of tumour sub-populations from single time-point samples. In doing so we demonstrate that high resolution spatial patterns of sub-population mixing in tumours can provide insights into early cancer dynamics. Second, we apply the same computational model to investigate regenerative dynamics within normal human liver, using spatial patterns of DNA mutations to show that long-lived progenitor cells, situated near the portal veins, drive punctuated expansions of liver cells. We provide new insights into normal liver dynamics in humans and establish a foundation for understanding how they may be modulated in diseased liver. Together, the results in this thesis demonstrate the knowledge which can be gained by leveraging the spatial context of cells, and provide a baseline for future spatial analyses of solid tissue. | en_US |
