Glioblastoma multiforme (GBM) is one of the most common and aggressive primary brain tumours, with a median patient survival time of 6-12 months in adults. It has been recently suggested that a typically small sub-population of brain tumour cells, in possession of certain defining properties of stem cells, is responsible for initiating and maintaining the tumour. More recent experiments have studied the interactions between this subpopulation of brain cancer cells and tumour microenvironmental factors such as hypoxia and high acidity. In this thesis a computational approach (based on Gillespie’s algorithm and cellular automata) is proposed to investigate the tumour heterogeneities that develop when exposed to various microenvironmental conditions of the cancerous tissue. The results suggest that microenvironmental conditions highly affect the characterization of cancer cells, including the self-renewal, differentiation and dedifferentiation properties of cancer cells.

Glioblastoma multiforme (GBM) is one of the most common and aggressive primary brain tumours, with a median patient survival time of 6-12 months in adults. It has been recently suggested that a typically small sub-population of brain tumour cells, in possession of certain defining properties of stem cells, is responsible for initiating and maintaining the tumour. More recent experiments have studied the interactions between this subpopulation of brain cancer cells and tumour microenvironmental factors such as hypoxia and high acidity. In this thesis a computational approach (based on Gillespie’s algorithm and cellular automata) is proposed to investigate the tumour heterogeneities that develop when exposed to various microenvironmental conditions of the cancerous tissue. The results suggest that microenvironmental conditions highly affect the characterization of cancer cells, including the self-renewal, differentiation and dedifferentiation properties of cancer cells.