Grant: Discovery Grants Program - Individual

When a foreign substance - a virus or bacterium - invades the tissues of a higher vertebrate, the organism defends itself through the immune response. The immune system is highly adaptive - it is able to "learn" to respond to an enormous variety of pathogens. In order for a disease to infect a host, it must circumvent the immune system (i.e. mutation), grow at a faster rate than it is killed by the immune system or, as in the case of HIV, infect the immune system itself. Over the last several decades much time and effort has been spent on basic research related to immune system/pathogen dynamics. These studies have led to major advances in our understanding of the immunology and molecular biology of pathogen infection and have aided in the development of vaccine/drug therapies that will best aid the immune system. Despite this progress, many basic questions have remained unanswered as they have been difficult to discern from clinical trials or are, currently, impossible to quantify in-vivo. Mathematical immunology, mathematical models of disease dynamics within an infected host, is currently the only technique that can address these questions. Mathematical immunology is a relatively new field, but interest in this area has been increasing rapidly due in large part to an enhanced collaboration between mathematicians and experimental scientists, particularly in the area of HIV/AIDS. The overriding objective of our work is to complement the ongoing work of experimentalists and clinicians by modeling in-host pathogenesis with a realistic level of biological detail. To this end, we will drastically increase the level of biological detail in models predict the in-host dynamics of HIV. We will also introduce immunological models for acute infections such as measles and influenza, aiming to elucidate the key immune system/pathogen interactions that enable the growth of the pathogen. Finally, we will develop comprehensive models, which combine in-host and epidemiological models, since immunological processes can greatly influence epidemiological outcomes.