Cancer is one of the biggest killers in the western world; every two minutes someone is diagnosed with cancer in the UK. Tumour growth and progression is a complex biological process, normally beginning with genetic mutations in a single cell. It starts with the early or avascular phase where growth is limited by nutrient diffusion, then the vascular stage where angiogenesis occurs to stimulate blood vessel production by the secretion of tumour angiogenesis factors and finally the metastasitic phase where the tumour spreads from the site of origin to distant sites around the body. While considering these events at the cellular level, these processes involve many microenvironment parameters like oxygen concentration, hypoglycaemia, acidity, hypoxia (lack of oxygen), cell-cell adhesion, cell migration and cell-extracellular matrix interactions. In this paper, a computational model is proposed which considered hypoxia as a microenvironment constraint of tumour growth. The model is built on two dimensional cellular automata grid and artificial neural network is considered for establishing signaling network of tumour cells. Each tumour cell can take its own decision in this model. A hypoxia impact was implemented in the model by varying different oxygen concentrations. The results show that hypoxia was introduced in the tumour mass due to lack of oxygen. The model measured tumour invasion and the number of apoptotic cells to support that hypoxia has a critical impacts on avascular tumour growth. This model could inform a better understanding of the impacts of hypoxia in tumour growth from the computational point of view.

Maspin (Mammary Serine Protease Inhibitor) is a non-inhibitory member of the serpin family of protease inhibitors that influences multiple cellular functions including adhesion, migration, and invasion in tumour malignancy. In this paper, we describe a computational model of the effects of exogenous maspin on cellular proliferation, migration and invasion. To date, the whole cellular mechanisms of maspin impact on cellular behaviors have not been clearly defined in any computational model. In this paper, we have used a feedforward artificial neural network to define cell signaling and cellular automata approach to define the each and every cell behavior. Results show that maspin reduces migration and invasion by 10-40% and 15-30% respectively; confirmed by published in vitro data. This is the first attempt to model the effects of maspin using a computational model to verify in vitro data. This will provide new insights into the tumour suppressive properties of maspin and inform the development of novel cancer therapies.