Evolutionary control of evolving populations: from molecular phenotypes to HIV therapy

Synopsis

Controlling an evolving population is an important task in modern molecular genetics, including in directed evolution to improve the activity of molecules, in breeding experiments, and in devising public health strategies to suppress pathogens. An optimal intervention should be designed by considering its impact over an entire evolutionary trajectory that follows. As a result, a seemingly suboptimal intervention at a given time can be globally optimal as it can open opportunities for desirable actions. Here, I will present a feedback control formalism to devise globally optimal artificial selection protocol to direct evolution of molecular phenotypes. I will show that artificial selection should counter evolutionary tradeoffs among multi-variate phenotypes to avoid undesirable outcomes in one phenotype by imposing selection on another. Control by artificial selection is challenged by our ability to predict evolution. Using an information theoretical framework, I will show that molecular time-scales for evolution under natural selection can inform how to monitor a population to acquire sufficient predictive information for an effective intervention. The interplay between prediction and control is a key in devising optimal strategies against evolving pathogens. I will present a predictive approach for HIV escape against broadly neutralizing antibodies (BnAbs). By leveraging this predictive model, I will propose optimal therapy approaches with combinations of BnAbs to sustain viremia at low levels and suppress evolutionary escape of HIV within patients.