Hepatitis C virus (HCV) infects 2-3% of the world's population. It is a major cause of liver morbidity and mortality and a scourge to global public health. Despite more than 20 years of research, only recently have effective treatments been developed and a vaccine remains elusive. The high cost of efficacious drug treatments severely limits their impact in the developing world, leaving prophylactic vaccination the best hope for global control. The high mutation rate of HCV coupled with its rapid replication rate enables it to rapidly escape host immune responses. In this thesis, I have employed tools from statistical physics, Bayesian inference, population dynamics, and high-performance computing to define empirical fitness landscapes and conduct viral dynamics simulations to determine vulnerable viral targets and rationally design vaccine immunogens. This computational design protocol will guide and accelerate vaccine development efforts by massively reducing the need for expensive and laborious trial-and-error experimentation. While this work has focused on HCV, the grander picture is that the tools I have developed will allow quick application of this methodology to other RNA viruses (some work has been done on HIV and influenza), magnifying the impact and implications of this work for global public health.
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In silico vaccine design for hepatitis C: A computational platform for fighting disease
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