用AI实时调控细胞电信号，实现组织形态的精准重塑。
AI-driven control of bioelectric signalling for real-time topological reorganization of cells
- 结合深度强化学习与实时生物反馈，动态调节细胞膜电位。
- 通过光遗传学等技术实现对生物电信号的高精度控制。
- 为再生医学和癌症治疗提供新思路，适合生物工程研究者。
理解并操控生物电信号可能推动发育生物学、再生医学和合成生物学的新进展。生物电信号是由离子跨膜运动引起的细胞膜电位梯度，参与调控细胞分化、增殖、凋亡及组织形态发生等关键过程。近期研究表明，可通过对这些信号进行调控，实现涡虫和青蛙等生物体的可控组织再生与形态重构。然而，在预测与控制膜电位（V_mem）的时空动态、解析其在组织器官发育中的调控作用，以及探索其在疾病治疗中的潜力方面仍存在显著知识空白。本文提出一种结合深度强化学习（DRL）框架与实验室自动化技术的实验方案，实现对生物电信号的实时操控，以引导组织再生与形态发生。该框架能持续与生物系统交互，并根据直接生物反馈调整策略。融合DRL与实时测量技术（如光遗传学、电压敏感染料、荧光报告蛋白及先进显微技术），可构建一个全面的精准生物电控制平台，有望提升对形态发生中生物电机制的理解，建立定量生物电模型，识别最简实验配置，并推动与再生医学和癌症治疗相关的生物电调控技术发展。最终目标是利用生物电信号开发新型生物医学与生物工程技术应用。
原文摘要 · Abstract (English)
Understanding and manipulating bioelectric signaling could present a new wave of progress in developmental biology, regenerative medicine, and synthetic biology. Bioelectric signals, defined as voltage gradients across cell membranes caused by ionic movements, play a role in regulating crucial processes including cellular differentiation, proliferation, apoptosis, and tissue morphogenesis. Recent studies demonstrate the ability to modulate these signals to achieve controlled tissue regeneration and morphological outcomes in organisms such as planaria and frogs. However, significant knowledge gaps remain, particularly in predicting and controlling the spatial and temporal dynamics of membrane potentials (V_mem), understanding their regulatory roles in tissue and organ development, and exploring their therapeutic potential in diseases. In this work we propose an experiment using Deep Reinforcement Learning (DRL) framework together with lab automation techniques for real-time manipulation of bioelectric signals to guide tissue regeneration and morphogenesis. The proposed framework should interact continuously with biological systems, adapting strategies based on direct biological feedback. Combining DRL with real-time measurement techniques -- such as optogenetics, voltage-sensitive dyes, fluorescent reporters, and advanced microscopy -- could provide a comprehensive platform for precise bioelectric control, leading to improved understanding of bioelectric mechanisms in morphogenesis, quantitative bioelectric models, identification of minimal experimental setups, and advancements in bioelectric modulation techniques relevant to regenerative medicine and cancer therapy. Ultimately, this research aims to utilize bioelectric signaling to develop new biomedical and bioengineering applications.
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