System dynamics and agent based simulation models can both be used to model and understand interactions of entities within a population. Our modeling work presented here is concerned with understanding the suitability of the different types of simulation for the immune system aging problems and comparing their results. We are trying to answer questions such as: How fit is the immune system given a certain age? Would an immune boost be of therapeutic value, e.g. to improve the effectiveness of a simultaneous vaccination? Understanding the processes of immune system aging and degradation may also help in development of therapies that reverse some of the damages caused thus improving life expectancy. Therefore as a first step our research focuses on T cells; major contributors to immune system functionality. One of the main factors influencing immune system aging is the output rate of naive T cells. Of further interest is the number and phenotypical variety of these cells in an individual, which will be the case study focused on in this paper.
Summary form only given. This work proposes a systematic approach to identify opportunities for cross-fertilization between two modeling paradigms: system dynamics and agent-based modeling. The motivation for this work is the authors' belief that there are gains to be made by crossing the boundaries between different domains of research, or different scientific approaches. This paper presents a comparison between the two modeling approaches, which is brought one step beyond the simple statement of the similarities and differences between them by introducing the novel aspect of taking a synergistic view specifically aimed at identifying a list of likely opportunities for cross-fertilization. The list presented here is not exhaustive and should be regarded more as a starting point than an ending point, and an invitation to other scientists to take such synergistic views even further.