The aim of our research is to demonstrate how mathematical and computational modelling can be utilized for better understanding of the cell cycle control and cancer for benefit of a better treatment of the disease. Our experimental work includes the quantitative measurement of the molecules being investigated and their activity in live cells. We have used a novel modelling methodology to construct a model which incorporates the activity of some 200 molecular species in a single dynamical system which can be solved using genetic algorithms designed to match experimental data to the model kinetics. We have developed a methodology for unifying the knowledge base, particularly in relation to molecular cell biology, molecular pathology, pharmacology and clinical trials. We have illustrated the approach using breast cancer as an example and have demonstrated its potential for the development of individual patient-based models to analyse treatment protocols at the molecular level, thereby predicting drug therapies based on individual patient molecular profiles. The methodology we describe is capable of absorbing a wide range of data types. It will lead to personalised cancer models and can predict patient's response to respective drug regimes.