ABSTRACT
The pace and unpredictability of evolution are critically relevant in a variety of modern challenges: combating drug resistance in pathogens and cancer1–3, understanding how species respond to environmental perturbations like climate change4, 5, and developing artificial selection approaches for agriculture6. Great progress has been made in quantitative modeling of evolution using fitness landscapes7–9, allowing a degree of prediction10 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 atoms11–14. 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 diseases15, 16, to the development of thermotolerant crops in preparation for climate change17, to accelerating bioengineering methods built on evolutionary models, like directed evolution of biomolecules18–20.
