Optimal evolutionary control for artificial selection on molecular phenotypes

TL;DR

This paper develops a feedback control framework for designing optimal artificial selection protocols to steer molecular evolution, considering stochastic trajectories and tradeoffs among multiple phenotypes.

Contribution

It introduces a formalism that integrates evolutionary dynamics, information theory, and control to optimize artificial selection in molecular evolution.

Findings

Optimal control protocols can effectively guide molecular evolution.

Counteracting tradeoffs among phenotypes improves evolutionary outcomes.

Evolutionary time-scales inform monitoring strategies for effective intervention.

Abstract

Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding experiments in animals and in plants, and in devising public health strategies to suppress evolving pathogens. An optimal intervention to direct evolution should be designed by considering its impact over an entire stochastic 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 in the future. Here, we propose a feedback control formalism to devise globally optimal artificial selection protocol to direct the evolution of molecular phenotypes. We show that artificial selection should be designed to 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 molecular evolution. We develop an information theoretical framework and show that molecular time-scales for evolution under natural selection can inform how to monitor a population in order to acquire sufficient predictive information for an effective intervention with artificial selection. Our formalism opens a new avenue for devising artificial selection methods for directed evolution of molecular functions.