Brydon Eastman | Applied Math, University of Waterloo
Machine Learning in Cellular Automata Models of Invasive Solid Tumours
Cell signalling in early tumour development is crucial to the geometry of the invading solid tumour. This complex process is impacted by numerous factors in the cellular micro-environment including the presence of cancerous stem cells, chemotactic agents, and oxygen concentration. All these factors influence the proliferation and invasive potential of a cancerous tumour. We investigate a selection of models that utilise partial differential equations, stochastic processes, and machine learning in hybrid techniques.