Quantitative Biosciences Thesis Proposal
Athulya Ram
School of Mathematics
Advisor: Prof. Leonid Bunimovich (School of Mathematics)
Open to the Community
Cross-immunoreactivity and local immunodeficiency
Thursday, September 23, at 9:30am
BlueJeans Link: https://bluejeans.com/903515967/5001
Committee members:
Prof. Christine Heitsch (School of Mathematics)
Prof. Rachel Kuske (School of Mathematics)
Prof. Joshua Weitz (School of Biology)
Abstract:
Cross-immunoreactivity is the phenomenon where antibodies that target some antigens can be generated through stimulation from other antigens. This is observed in viral infections like Hepatitis-C, HIV, dengue, influenza, etc and leads to local immunodeficiency, where some viral variants (called persistent) hide from the immune system of the host body by stimulating antibodies that target other variants (called altruists). My work will build on the discovery of minimal networks - the smallest cross-immunoreactivity networks (CRN) that exhibit local immunodeficiency; on analysis of data on CRNs for populations of people with chronic and acute forms of Hepatitis C; and building mathematical models which are relevant for generating such data.
When a new viral variant is added to a minimal CRN or when two minimal CR networks get connected, the roles of the variants could change - i.e. a persistent virus could get kicked out of the network and a new virus could become persistent. These changes depend on the replication rates of the viruses involved and on the way how the new connections are made. Importantly, the addition of a new virus could create the state of local immunodeficiency in a network that previously did not have such a state. I also look at the maximal load of persistent viruses that can be supported by a single altruist and conclude that altruists work autonomously to support persistent viruses in a network, i.e. they do not cooperate and do not compete either.
The next part of the study looks at real patient data from Hepatitis-C virus infection and tries to find qualitative differences between acute (recently established) and chronic infections through network analysis. These data are supplied by the CDC. In the future, I plan to extend this study to HIV infection and possibly influenza.
Together, this work supports my aims to study growing cross-immunoreactivity networks - how they gain or lose the state of stable local immunodeficiency, the conditions under which new variants can become persistent, how an infection proceeds to be chronic, and how these properties compare across different viruses with cross-immunoreactivity.
