Dynamical modelling of somatic genomes

Cancers are complex and chaotic systems. It is becoming apparent that no two cells in a cancer are genetically identical or follow the same evolutionary trajectory. Chromosomal instability (CIN) is one way that cells generate this complexity and is a hallmark of all cancer and ageing. In cancer, it increases the level of variation available to cells and gives rise to intra-tumour genetic hetereogeneity, which makes the disease more agressive, drug tolerant, and harder to treat. We are still far from a complete understanding of how cells undergoing CIN evolve over time, in particular, we do not know how populations of cancer cells evolve and how selection acts to change these properties. Understanding this normal evolutionary behaviour will be key to separating the functional and non-functional aspects of intra-tumour heterogeneity. We will tackle this problem by understanding cancer as an emergent complex system, and use simple dynamic stochastic models to capture the essential biological features of the processes underlying CIN, including chromosome gain and loss, structural change, and genome doubling. We will use the vast amount of NGS data already available to fit these models using Bayesian inference and infer the evolutionary aspects of CIN in healthy and cancerous tissues.