An understanding of antiviral drug resistance is important in the design of effective drugs. Comprehensive features of the interaction between drug designs and resistance mutations are difficult to study experimentally because of the very large numbers of drugs and mutants involved. We describe a computational framework for studying antiviral drug resistance. Data on HIV-1 protease are used to derive an approximate model that predicts interaction of a wide range of mutant forms of the protease with a broad class of protease inhibitors. An algorithm based on competitive coevolution is used to find highly resistant mutant forms of the protease, and effective inhibitors against such mutants, in the context of the model. We use this method to characterize general features of inhibitors that are effective in overcoming resistance, and to study related issues of selection pathways, cross-resistance, and combination therapies.
We apply a genetic algorithm whose genotypes represent simple grammars to search for families of compare-exchange networks that are merging networks. The grammars are restricted to be of a simple form based on prior knowledge about the search domain. Finding merging networks in this fashion leads to the discovery of networks that also sort if the data is cycled through them a small number of times. Random network and grammar generation results show that the problem is difficult and suggest some conjectures about how the genetic algorithm is operating for this problem. The genetic algorithm finds the best-known network of the kind for which we search, and further finds a novel, slightly suboptimal network that turns out to be an interesting combination of ideas from known, theoretically derived networks.