Controlling the speed and trajectory of evolution with counterdiabatic driving
Controlling the speed and trajectory of evolution with counterdiabatic driving
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DOI：
10.1038/s41567-020-0989-3
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发表时间：
2020-08-24
期刊：
影响因子：
19.6
通讯作者：
Hinczewski, Michael
中科院分区：
文献类型：
--
作者：
Iram, Shamreen;Dolson, Emily;Hinczewski, Michael
进化的不可预测性使得很难处理耐药性，因为在治疗过程中，可能会有我们无法预测的突变。作者建议使用量子方法来控制潜在进化结果的速度和分布。进化的速度和不可预测性在各种现代挑战中至关重要，例如在病原体和癌症中打击耐药性，了解物种对环境的反应诸如气候变化和开发农业人工选择方法之类的扰动。使用健身景观对进化的定量建模取得了巨大进展，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的细粒度控制仍然难以捉摸。我们建议使用最初在完全不同的上下文 - 苏绝热驾驶中发展的思想来实现这一目标，以控制量子状态的行为，以用于量子计算和操纵超电原子等应用。在生物学背景下，我们首次实施这些想法，我们展示了一组外部控制参数（即不同的药物浓度和类型，温度和养分）如何指导沿指定路径和人群中基因型的概率分布时间间隔。这种控制水平，允许对进化速度和轨迹的经验优化，具有无数的潜在应用，从增强疾病的自适应疗法到耐热剂农作物的开发，以准备气候变化，到加速生物引起的生物工程学方法，例如基于进化模型，例如有指导性的进化。生物分子。
The unpredictability of evolution makes it difficult to deal with drug resistance because over the course of a treatment there may be mutations that we cannot predict. The authors propose to use quantum methods to control the speed and distribution of potential evolutionary outcomes.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.