Getting Started in Computational Immunology

Abstract

The immune system acts across multiple scales involving complex interactions and feedback, from somatic modifications of DNA to the systemic inflammatory reaction. Computational modeling provides a framework to integrate observational data collected from multiple modes of experimentation and insight into the immune response in health and disease. This Message attempts to illustrate how different computational methods have been integrated with experimental observations to study an immunological question from multiple perspectives by focusing on a very particular, though fundamental, component of adaptive immunity: B cells and affinity maturation (Figure 1). B cells bind foreign antigens through their Immunoglobulin (Ig) receptor. Affinity maturation is the process by which B cell receptors that initially bind antigen with low affinity are modified through cycles of somatic mutation and affinity-dependent selection to produce high-affinity memory and plasma cells. How this process can reliably generate orders of magnitude increases in affinity over a period of weeks is one of the many questions where computational modeling has made important contributions (for example, the cyclic re-entry model [1]). Yet, even the seemingly straightforward matter of detecting antigen-driven selection remains controversial, and such fundamental questions as whether increased proliferation or decreased death drives the preferential expansion of higher-affinity B cell mutants remain unanswered. A good biological introduction to the immune system is available on the NIH website [2], while more detailed information can be found in any number of textbooks [3]. An animation by Julian Kirk-Elleker provides a visual introduction to the affinity maturation process (http://web.mac.com/patrickwlee/Antibody-affinity_maturation/Movie.html). The kinds of computational techniques described here have been widely applied in other areas of immunology, including the innate response [4],[5], viral dynamics [6], and immune memory [7]. A classic introduction to computational immunology geared to the more mathematically inclined was written by Perelson and Weisbuch [8]. The rapidly expanding area of immunoinformatics was covered in a recent issue of PLoS Computational Biology [9], and several other applications were explored in a 2007 volume of Immunological Reviews (216) devoted to quantitative modeling of immune responses. Figure 1 A wide range of experimental techniques are used in combination with computational modeling to probe the process of affinity maturation at multiple scales (from DNA to tissue).