Opportunities and Challenges Provided by Boolean Modelling of Cancer Signalling Pathways

Cancer is a multifactorial disease, which involves intricate signalling pathways leading to different cellular processes such as survival, proliferation, differentiation or apoptosis. This diversity significantly complicates cancer investigation and treatment. There have been studies on investigating signalling in cancer, but it is difficult to rely solely on molecular biology approaches in order to predict the properties of cancer pathways. Therefore, mathematical modelling has been increasingly used to understand tumour progression, direct the design of new experiments by introducing likely candidates for clinical investigation and present novel approaches to cancer therapy. Different types of modelling techniques, including static and dynamic methods, have been used to model processes that occur in cancer. Boolean models have attracted a large interest in cancer research due to their efficiency and scalability. We here review the use of Boolean modelling for cancer, highlighting the facts that this technique is efficient at generating predictions and that the gain brought by their scalability and flexibility often offsets the lack of quantitative values upon which they are based.