Controlling the speed and trajectory of evolution with counterdiabatic driving

TL;DR

This paper introduces a novel method inspired by quantum control techniques to steer evolutionary trajectories in biological populations, enabling precise manipulation of evolutionary speed and paths for diverse applications.

Contribution

It adapts counterdiabatic driving from quantum physics to biological evolution, allowing external control of genotype distributions over time.

Findings

Demonstrates how external parameters can guide evolutionary trajectories.

Shows potential for optimizing adaptive therapies and crop development.

Provides a framework for empirical control of evolution.

Abstract

The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer, understanding how species respond to environmental perturbations like climate change, and developing artificial selection approaches for agriculture. Great progress has been made in quantitative modeling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and the distributions of these trajectories remains elusive. We propose an approach to achieve this using ideas originally developed in a completely different context: counterdiabatic driving to control the behavior of quantum states for applications like quantum computing and manipulating ultra-cold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (i.e. varying drug concentrations / types, temperature, nutrients) can guide the probability distribution of genotypes in a population along a specified path and time interval. This level of control, allowing empirical optimization of evolutionary speed and trajectories, has myriad potential applications, from enhancing adaptive therapies for diseases, to the development of thermotolerant crops in preparation for climate change, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules.