Modelling of tissue level carcinogenesis through hybrid, biophysical, and executable approaches

Cancer is a major health concern, with one in three adults developing the disease over their lifetime. However, on a cellular level, the probability of a cell developing into a tumour is very low. Cells prevent the development of tumours through a combination of mechanisms, which operate over different scales. Within a single cell, proteins and genes form signalling pathways which reliably interpret and respond to both the cell state and the cell environment, including specific signals passed from other cells. At the same time, within a tissue, the movements and locations of cells can add a further layer of control, by limiting the communication between cells and mixing populations of cells. I will build mathematical representations (a “model”) of the early stages of tumour formation in cancer which bridges these two phenomena. By running simulations using this model, I will learn more about how the signalling and physical components combine to control cell growth and death in the oesophageal epithelium. I will further mutate and wound my model, to explore how these events can cause a breakdown of the controlling mechanisms and lead to tumour growth.

Cancer is an important and widespread disease, with roughly one in three adults developing the illness in their lifetime. However, the likelihood of a single cell from the hundreds of trillion cells in the adult human body at any one time developing into a tumour is very low due to intrinsic mechanisms of suppression. Stem cells in the body are made robust to deleterious mutations by a large number of poorly characterised mechanisms. One such mechanism for limiting the damage done by mutations is the competition of stem cells within a population. Whilst recent experimental evidence has given us insight into this competition, observing the dynamics of tissue development experimentally is arduous, and requires use of animals.
The objective of this program is to develop models which give insights into experimentally inaccessible phenomena in the development of cancers, by coupling developmental and physical effects within cells. Some of these models will couple the physical dynamics of cells in the tissue with models of the underlying signalling networks to give us insights into the competition between different mutations. I will use these models to explore the nature of tissue growth and study how external events, such as wounding, can alter this competition and trigger stable populations of cells to develop into a tumour. By explicitly modelling the 3D structure of the tissue, the models will allow us to gain insights into the early stages of tumour formation by showing how changes in stratification rate and shape of the basal layer of stem cells are correlated to changes in cell proliferation rate. Alongside this, I will model cellular localisation processes, working on the dynamics and behaviours of mitochondria in the control of metabolic pathways in cancer cells.
These insights will both suggest new experiments, and highlight the most fruitful lines of experimental enquiry. As such, this programme of work will involve extensive collaboration with different members of the cancer unit. By coupling the expertise of the group, with my own skillset, I will develop a set of approaches and discoveries which guide and support our understanding of cancer development.