标题： 定向进化有效选择出基于DNA的物理储层计算网络，能够完成多项任务 显示英文标题

标题： Directed evolution effectively selects for DNA based physical reservoir computing networks capable of multiple tasks

摘要： DNA和其他生物聚合物正在被研究作为新的计算基质，以替代基于硅的数字计算机。 然而，现有的自上而下设计的生物分子相互作用网络仍然具有挑战性，并未充分利用生物分子自组装的能力。 在计算领域之外，定向进化已被用作一种工具，用于目标导向优化DNA序列。 在这里，我们提出将定向进化与基于DNA的储备计算相结合，以实现材料内的优化和适应。 模拟通过DNA链连接的胶体珠网络，展示了一个能够执行非线性时间序列预测任务的物理储备，包括Volterra级数和Mackey-Glass混沌动力学。 储备计算性能，通过归一化均方误差（NMSE）量化，强烈依赖于网络拓扑，表明任务特定的最优网络配置。 使用遗传算法进化DNA编码的网络连接，有效识别了表现良好的储备网络。 定向进化在多个任务中提高了储备性能，优于随机网络选择。 值得注意的是，对不同任务的顺序训练使得储备种群能够在先前任务上保持性能。 我们的研究结果表明，DNA-珠网络提供了储备计算所需的足够复杂性，且定向进化能够稳健地优化性能。 显示英文摘要

摘要： DNA and other biopolymers are being investigated as new computing substrates and alternative to silicon-based digital computers. However, the established top-down design of biomolecular interaction networks remains challenging and does not fully exploit biomolecular self-assembly capabilities. Outside of the field of computation directed evolution has been used as a tool for goal directed optimization of DNA sequences. Here, we propose integrating directed evolution with DNA-based reservoir computing to enable in-material optimization and adaptation. Simulations of colloidal beads networks connected via DNA strands demonstrate a physical reservoir capable of non-linear time-series prediction tasks, including Volterra series and Mackey-Glass chaotic dynamics. Reservoir computing performance, quantified by normalized mean squared error (NMSE), strongly depends on network topology, suggesting task-specific optimal network configurations. Implementing genetic algorithms to evolve DNA-encoded network connectivity effectively identified well-performing reservoir networks. Directed evolution improved reservoir performance across multiple tasks, outperforming random network selection. Remarkably, sequential training on distinct tasks resulted in reservoir populations maintaining performance on prior tasks. Our findings indicate DNA-bead networks offer sufficient complexity for reservoir computing, and that directed evolution robustly optimizes performance.