Department of Computer Science, Organismal and Evolutionary Biology Research Programme, University of Helsinki, Helsinki 00014, Finland.
Institute of Biotechnology, University of Helsinki, Helsinki 00014, Finland.
J R Soc Interface. 2023 Jan;20(198):20220744. doi: 10.1098/rsif.2022.0744. Epub 2023 Jan 4.
Evolutionary prediction and control are increasingly interesting research topics that are expanding to new areas of application. Unravelling and anticipating successful adaptations to different selection pressures becomes crucial when steering rapidly evolving cancer or microbial populations towards a chosen target. Here we introduce and apply a rich theoretical framework of optimal control to understand adaptive use of traits, which in turn allows eco-evolutionarily informed population control. Using adaptive metabolism and microbial experimental evolution as a case study, we show how demographic stochasticity alone can lead to lag time evolution, which appears as an emergent property in our model. We further show that the cycle length used in serial transfer experiments has practical importance as it may cause unintentional selection for specific growth strategies and lag times. Finally, we show how frequency-dependent selection can be incorporated to the state-dependent optimal control framework allowing the modelling of complex eco-evolutionary dynamics. Our study demonstrates the utility of optimal control theory in elucidating organismal adaptations and the intrinsic decision making of cellular communities with high adaptive potential.
进化预测和控制是越来越有趣的研究课题，其应用领域正在不断扩展。在引导快速进化的癌症或微生物种群朝着选定的目标发展时，揭示和预测对不同选择压力的成功适应变得至关重要。在这里，我们引入并应用了一个丰富的最优控制理论框架，以了解特征的适应性使用，这反过来又允许进行基于生态进化的种群控制。我们使用适应性代谢和微生物实验进化作为案例研究，展示了仅通过种群动态随机波动如何导致滞后时间进化，这在我们的模型中表现为一种突现属性。我们进一步表明，在连续传代实验中使用的周期长度具有实际意义，因为它可能导致对特定生长策略和滞后时间的无意识选择。最后，我们展示了如何将频率依赖选择纳入状态依赖最优控制框架，从而能够对复杂的生态进化动态进行建模。我们的研究表明，最优控制理论在阐明具有高适应潜力的生物体适应性和细胞群落的内在决策方面具有实用性。
