  Although there are various types of cancer treatments, none of these
currently take into account the effect of ageing of the immune system and hence
altered responses to cancer. Recent studies have shown that in vitro
stimulation of T cells can help in the treatment of patients. There are many
factors that have to be considered when simulating an organism's
immunocompetence. Our particular interest lies in the study of loss of
immunocompetence with age. We are trying to answer questions such as: Given a
certain age of a patient, how fit is their immune system to fight cancer? Would
an immune boost improve the effectiveness of a cancer treatment given the
patient's immune phenotype and age? We believe that understanding the processes
of immune system ageing and degradation through computer simulation may help in
answering these questions. Specifically, we have decided to look at the change
in numbers of naive T cells with age, as they play a important role in
responses to cancer and anti-tumour vaccination. In this work we present an
agent-based simulation model to understand the interactions which influence the
naive T cell populations over time. Our agent model is based on existing
mathematical system dynamic model, but in comparisons offers better scope for
customisation and detailed analysis. We believe that the results obtained can
in future help with the modelling of T cell populations inside tumours.
