The pace and unpredictability of evolution are critically relevant in a variety of modern challenges, such as 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 modelling of evolution using fitness landscapes, allowing a degree of prediction for future evolutionary histories. Yet fine-grained control of the speed and 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 behaviour of quantum states for applications like quantum computing and manipulating ultracold atoms. Implementing these ideas for the first time in a biological context, we show how a set of external control parameters (that is, varying drug concentrations and types, temperature and 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.

进化的速度和不可预测性与各种现代挑战至关重要，例如与病原体和癌症中的耐药性作斗争，了解物种如何应对气候变化等环境扰动以及发展农业的人工选择方法。在使用适应度景观进行进化的定量建模方面，已经取得了巨大进展，从而可以对未来的进化历史进行一定程度的预测。然而，对这些轨迹的速度和分布进行细粒度的控制仍然难以实现。我们提出了一种使用最初在完全不同的上下文中开发的思想来实现这一目标的方法-绝热驱动来控制量子状态的行为，以用于诸如量子计算和操纵超冷原子的应用。我们首次在生物学环境中实施这些想法，我们展示了一组外部控制参数（即，变化的药物浓度和类型，温度和营养素）如何指导基因型在特定路径上的概率分布，以及时间间隔。这种控制水平允许对进化速度和轨迹进行经验优化，从增强对疾病的适应性疗法到为应对气候变化做准备的耐高温作物的发展，到基于进化模型的生物工程方法的加速应用，如定向进化等，都有许多潜在的应用。生物分子。温度和养分）可以指导基因型在特定路径和时间间隔内的概率分布。这种控制水平允许对进化速度和轨迹进行经验优化，从增强对疾病的适应性疗法到为应对气候变化做准备的耐高温作物的发展，到基于进化模型的生物工程方法的加速应用，如定向进化等，都有许多潜在的应用。生物分子。温度和养分）可以指导基因型在特定路径和时间间隔内的概率分布。这种控制水平允许对进化速度和轨迹进行经验优化，从增强对疾病的适应性疗法到为应对气候变化做准备的耐高温作物的发展，到基于进化模型的生物工程方法的加速应用，如定向进化等，都有许多潜在的应用。生物分子。