James Moore, Jr. Slides, Video, Abstract: Lymph nodes are immune information collection and transfer junctions, where immune cells are housed in sufficient numbers and varieties to develop effective immune responses.  It is therefore the job of the lymphatic system to gather information, including free antigen, antigen presenting cells, cytokines and other immune cells/signals, from the periphery and deliver them to the node for further processing.  This requires an exquisitely complex combination of active pumping, cell/antigen transport, and biological signaling between multiple cell types.  We have performed a series of experiments and computational simulations of flow patterns in afferent lymphatic vessels and lymph nodes.  Agent-based models have been developed to explore the role of lymph node swelling in T cell population dynamics.  Pumping dynamics of afferent vessels, and therefore the delivery of immune information to lymph nodes, are strongly affected by variations in local pressure, including the external pressure and vasoactive effects of vaccinations.  A better understanding of these transport phenomena could lead to more relevant criteria for designing methods to modulate the immune system effectively for health benefit, including vaccines.

Denise Kirschner Slides, Video, Abstract: Tuberculosis (TB) is one of the world’s deadliest infectious diseases. Caused by the pathogen Mycobacterium tuberculosis (Mtb), the standard regimen for treating TB consists of treatment with multiple antibiotics for at least six months. There are a number of complicating factors that contribute to the need for this long treatment duration and increase the risk of treatment failure. The structure of granulomas, lesions forming in lungs in response to Mtb infection, create heterogeneous antibiotic distributions that limit antibiotic exposure to Mtb. We can use a systems biology approach pairing experimental data from non-human primates with computational modeling to represent and predict how factors impact antibiotic regimen efficacy and granuloma bacterial sterilization. We utilize an agent-based, computational model that simulates granuloma formation, function and treatment, called GranSim. A goal in improving antibiotic treatment for TB is to find regimens that can shorten the time it takes to sterilize granulomas while minimizing the amount of antibiotic required. With the number of potential combinations of antibiotics and dosages, it is prohibitively expensive to exhaustively search all combinations to achieve these goals. We present a framework to search for optimal regimens using GranSim. Overall, we present a computational tool to evaluate antibiotic regimen efficacy while accounting for the complicating factors in TB treatment to strengthen our ability to predict new regimens that can improve clinical treatment of TB.