  Many advances in research regarding immuno-interactions with cancer were
developed with the help of ordinary differential equation (ODE) models. These
models, however, are not effectively capable of representing problems involving
individual localisation, memory and emerging properties, which are common
characteristics of cells and molecules of the immune system. Agent-based
modelling and simulation is an alternative paradigm to ODE models that
overcomes these limitations. In this paper we investigate the potential
contribution of agent-based modelling and simulation when compared to ODE
modelling and simulation. We seek answers to the following questions: Is it
possible to obtain an equivalent agent-based model from the ODE formulation? Do
the outcomes differ? Are there any benefits of using one method compared to the
other? To answer these questions, we have considered three case studies using
established mathematical models of immune interactions with early-stage cancer.
These case studies were re-conceptualised under an agent-based perspective and
the simulation results were then compared with those from the ODE models. Our
results show that it is possible to obtain equivalent agent-based models (i.e.
implementing the same mechanisms); the simulation output of both types of
models however might differ depending on the attributes of the system to be
modelled. In some cases, additional insight from using agent-based modelling
was obtained. Overall, we can confirm that agent-based modelling is a useful
addition to the tool set of immunologists, as it has extra features that allow
for simulations with characteristics that are closer to the biological
phenomena.
