Modeling the adaptive immune system: predictions and simulations

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

Improved understanding of the immune systems, and its population-wide variation, is one of the major challenges in the next decade within biology and medicine. Many of the steps by which the immune system deal with infectious agents and disease can now successfully be modeled by computational techniques, and it is clear that the theoretical approaches will be a major player in this area, adding a systems view to the massive experimental effort being carried out at the moment. In this review, we have summarized how a number of bioinformatics tools that use genomic sequences as input to predict epitopes, have been developed over the past decade. At the same time, theoretical models have been developed that describe the dynamics of different immune-cell populations and their interactions with microbes (Borghans and de Boer, 2007; Carneiro et al., 2007; Davenport et al., 2007) . These models have been used to interpret experimental findings where timing is of importance, such as the interval between administration of a vaccine and infection with the microbe that the vaccine is intended to protect against. Moreover, these dynamic models allowed for generating a quantitative picture of immune system kinetics and diversity during health and disease. The quantitative approach is necessary to understand the functioning of the immune system, which consists of many different cell types and molecules interacting in complicated regulatory pathways involving positive and negative feedback loops. Surprisingly little is known about the population dynamics, i.e. the production rates, division rates and distribution of life spans of mouse or human lymphocyte populations. As a consequence, fundamental questions like the maintenance of memory, the maintenance of a diverse naive repertoire and the role of homeostatic mechanisms, remain largely unresolved. Having so little insight in the normal lymphocyte population dynamics also hampers our understanding of immune responses during disease and immune reconstitution after therapeutic interventions such as chemotherapy, irradiation and/or bone marrow transplantation. Several areas in immunology call for a better interpretation of data by means of theoretical models. A simple PubMed search reveals that at least 10% of the recent papers in the immunological literature involve labeling experiments in which lymphocytes are labeled radioactively, with deuterium, or with dyes. However, the interpretation of such labeling data is controversial and is notoriously difficult (Boer et al., 2003a, b; Deenick et al., 2003; Gett and Hodgkin, 2000; Hellerstein, 1999; Mohri et al., 1998; Mohri et al., 2001; Revy et al., 2001; Ribeiro et al., 2002) , which emphasizes the enormous demand to develop a quantitative mathematical approach to immunology. Similar examples of how difficult it is to properly interpret kinetic data come from the attempts to characterize the division history of cells from the length of the telomeres, or from the presence of autosomal DNA circles (TRECs) that are formed in the thymus (Boer and Noest, 1998; Douek et al., 1998; Dutilh and de Boer, 2003; Hazenberg et al., 2000; Hazenberg et al., 2003) .