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 trade-offs among multivariate 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 timescales 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.
- Received 24 May 2020
- Revised 2 December 2020
- Accepted 11 January 2021
DOI:https://doi.org/10.1103/PhysRevX.11.011044
Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Open access publication funded by the Max Planck Society.
Published by the American Physical Society
Physics Subject Headings (PhySH)
Popular Summary
Controlling an evolving population is central in modern molecular genetics, including in directed evolution by artificial selection to improve function, and in devising public health strategies to suppress evolving pathogens. Current approaches in the lab are often ad hoc and, at best, use local evolutionary optimization to direct a population toward a desired target. However, local optimization can be ineffective, especially because of the ubiquity of evolutionary trade-offs in multivariate phenotypic traits. Thus, artificial selection should be devised by considering its impact over an entire evolutionary path to reach a target. Based on this principle of optimality, we introduce a control formalism to direct stochastic evolution of multivariate molecular phenotypes, such as the thermal stability and catalytic activity of an enzyme, to avoid undesirable outcomes in one phenotype by imposing selection on another.
Evolutionary control is challenged by the limited predictability of evolution due to stochastic effects at the molecular or environmental level. Predictability depends on the time span over which we aim to forecast and control evolution. To capture this, we introduce an information theoretical approach to gauge the efficacy of an evolutionary control based on the degree of predictive evolutionary information. Importantly, we show that the optimal schedule to monitor a population and to intervene with its evolution should be informed by the relevant timescales of molecular evolution under natural selection.
Our formalism brings together concepts from statistical physics and population genetics to introduce a new paradigm of optimal control for devising artificial selection schemes that control molecular evolution.