Computational models to demonstrate the effects of maspin on tumour growth

Cancer metastasis is a complex multistep process which allows cancer cells to establish new tumours in distant organs. This involves cell migration and invasion; it is what makes cancer a fatal disease. The efficiency of most cancer treatments depends on metastasis suppression. Maspin is a type II tumour metastasis suppressor which has multiple cellular effects. Maspin has been shown to reduce cell migration, invasion, proliferation and angiogenesis, and increase apoptosis and cell–cell adhesion in in vitro and in vivo experiments. In this research, the aim was to develop computational models to understand and investigate the impact of maspin on tumour growth and evolutionary dynamics. The first model presents a hybrid in silico investigation of solid tumour growth framework where the exogenous effects of maspin were modelled into cell migration and tumour invasion process in cellular level. Artificial neural network and cellular automata paradigms have been used to model this framework. The results show that maspin strongly influenced tumour morphology by decreasing cell migration and invasion and increasing apoptosis in the model. The data verification method showed significant resemblance of our model with in vitro experimental data. The second model presents a subcellular investigation where microscopic images obtained from novel in vitro experiments were analysed by a quantitative image analysis method. A hybrid marker controlled watershed technique was adopted for image segmentation and important descriptors have been calculated for shape analysis. The quantitative results show that maspin has a significant influence on cellular cytoskeletal architecture during the migration process. Finally, this thesis provides new insights into the tumour suppressive properties of maspin, prognostic significance and informs the development of novel cancer therapies.