Modeling the adaptive immune system: predictions and simulations

Lundegaard, Claus; Lund, Ole; Kesmir, Can; Brunak, Søren; Nielsen, Morten

Integrating the dynamic (using mathematical models and computer simulations) and bioinformatics approaches clearly could lead to a better understanding of the immune responses and their role during normal, disease and reconstitution states, where both timing and sequence specificity are highly significant. Diseases that are characterized by complex interactions between the host cellular immune system and evolving pathogens such as HIV infection, or diseases where molecular similarities between self and non-self are important such as in autoimmune diseases could be investigated in such integrated models. Complex generalized cellular automata have been proposed as models of the immune system (Kohler et al., 2000; Seiden and Celada, 1992) . These methods have now developed to a stage where it is possible successfully to simulate the outcome of cancer vaccine protocols using a mouse simulation model (Castiglione and Piccoli, 2007; Lollini et al., 2006; Motta et al., 2005; Pappalardo et al., 2006) . In a recent paper, Rapin et al. (2006) outline a framework for integration of these bioinformatics and simulation approaches by developing a simple model in which HIV dynamics are correlated with genomics data. This model is the first one where, the fitness of wild-type and mutated virus is assessed by means of a sequence-dependent scoring matrix that links protein sequences to growth rates of the virus. Further refinements of these approaches may involve increasing the spatial resolution by including different tissues and their geometry.