Modern technology now allows the analysis of immune responses and host-pathogen interactions at a global level, across scales ranging from intracellular signaling networks, to individual cell behavior, to the functioning of a tissue, organ, and even the whole organism. The challenge is not only to collect the large amounts of data such methods permit, but also to organize the information in a manner that enhances our understanding of how the immune system operates or pathogens affect their hosts.? To do this, it is necessary to develop detailed quantitative models that can be used to predict the behavior of a complex biological system, whose properties help explain the mechanistic basis for physiological and pathological responses to infection or vaccination, and that can be employed to design better therapies or vaccines.? A major roadblock on the way to quantitative computer modeling of cellular behavior has been that the translation of qualitative biological models into computational models required the intervention of engineers/mathematicians as interfaces between biological hypotheses and their theoretical and computational representations. The software being developed by the computational biology group of the PSIIM eliminates the necessity of having this translation done by a person and thereby reduces the risk of oversimplification of biological mechanisms or the loss of important details in the course of translation by a non-biologist. The software offers an intuitive graphical interface combined with state-of-the-art simulation technology. ? One focus of the development in 2007/2008 has been the improvement of the softwares capabilities to perform spatially highly resolved simulations that capture important morphological aspects of cellular signaling. Another focus of activity has been the creation of a database and database interface system that couples the computational models to experimental data and externally generated proteomic information. Finally, efficient stochastic simulation capabilities are currently being added to the softwares algorithms.? ? At a higher biological scale, we are developing mathematical and computational models of mechanisms of T cell differentiation and homeostasis. In healthy individuals, a balance between cell death, proliferation and differentiation into T cell subtypes with certain effector functions and proliferative potential maintains a sufficient population of recirculating and peripheral effector cells.? In HIV / SIV infected individuals / Rhesus Macaques damage to the lymphoid system and the initial viral killing of peripheral T cells especially in the mucosal tissues lead to an immediate deficit of effector cells in the periphery which persists even after treatment with anti-retroviral drugs because the restorative capacity of the central (recirculating) T cells is destroyed.? We are trying to answer the following questions: What leads to a destabilization of T cell proliferation and differentiation in HIV / SIV? How does this manifest itself as altered proliferation/death/differentiation rates of T cells? What are the differences between CD4 and CD8 cells? To try to answer these questions we develop mathematical models describing the division, death and differentiation of T cells. These models can be represented as sets of coupled linear ordinary differential equations for the dynamics of the sizes of the different cell populations. We then try to determine which model is capable of most precisely matching the experimental data through parameter optimizations (fitting).
