Using UML to Model EAE and Its Regulatory Network

Experimental Autoimmune Encephalomyelitis (EAE) is an autoimmune disease in mice which serves as a model for multiple sclerosis in humans [4, 5]. The disease constitutes the direction of immunity towards myelin, an insulatory material that covers neurons. The consequential damage to the central nervous system (CNS) can lead to paralysis and death [6]. EAE can be spontaneously induced by immunisation with myelin basic protein (MBP, a myelin derivative) and complete Freund's adjuvant. The immuni-sation prompts the expression of MBP peptides on MHC molecules by antigen presenting cells (APCs), and the consequent activation of MBP-reactive T cells. The activated T cells migrate to the CNS parenchyma where their secretion of type 1 cytokines promotes the destruction of myelin. A network of immune cell interactions operates to counter EAE. This regulatory network consists of CD4 + and CD8 + regulatory T cells (Tregs). The natural lifecycle of MBP-reactive CD4Th1 cells leads to their physiological apoptosis and subsequent phagocytosis by APCs. The peptides derived from CD4Th1 cells, when presented on MHC, prompt the activation of CD4 + and CD8 + Tregs. The CD8Tregs, with prior help from CD4Tregs, can induce the apoptosis of activated MBP-reactive CD4Th1 cells. The resulting population reduction permits the expansion of CD4Th2 cells which do not promote debilitating destruction of the CNS. It is essential that a coherent understanding of the biological domain is obtained to construct an accurate representation of the system [7]. In line with others, for example [2, 1, 3], we have selected the UML as the tool with which to develop our models, before we move to an agent based simulation. We have completed a first pass in modelling the biological domain and wish to highlight certain issues that we have found when employing the UML in this context. We have found that the construction of our models has raised various questions of the system's operation; the immunological literature typically reports what does happen in a certain experimental setup for a particular event to manifest , it does not indicate all of what can happen under altered conditions. To correctly model and simulate the system we must know the latter as well as the former.