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 immunisation 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

Techniques for grounding agent-based simulations in the real domain: a case study in experimental autoimmune encephalomyelitis

For computational agent-based simulation, to become a serious tool for investigating biological systems requires the implications of simulation-derived results to be appreciated in terms of the original system. However, epistemic uncertainty regarding the exact nature of biological systems can complicate the calibration of models and simulations that attempt to capture their structure and behaviour, and can obscure the interpretation of simulation-derived experimental results with respect to the real domain. We present an approach to the calibration of an agent-based model of experimental autoimmune encephalomyelitis (EAE), a mouse proxy for multiple sclerosis (MS), which harnesses interaction between a modeller and domain expert in mitigating uncertainty in the data derived from the real domain. A novel uncertainty analysis technique is presented that, in conjunction with a latin hypercube-based global sensitivity analysis, can indicate the implications of epistemic uncertainty in the real domain. These analyses may be considered in the context of domain-specific knowledge to qualify the certainty placed on the results of in silico experimentation.</p