Evolutionary Escape on Fitness Landscapes

The evolution of HIV within individual patients is associated with disease progression and failure of antiretroviral drug therapy. Using graphical models we describe the development of HIV drug resistance mutations and show how these models improve predictions of the clinical outcome of combination therapy. We present combinatorial algorithms for computing the risk of escape of an evolving population on a given fitness landscape. The geometry of fitness landscapes and the underlying gene interactions are analyzed in an attempt to generalize the notion of pairwise epistasis to higher-order genetic systems. Finally, we discuss the new and exciting prospects for analyzing viral genetic variation that arises from recent pyro-sequencing technology.