Abstract Overview

• The paper addresses the challenge of regenerating complex organs (like limbs) and creating “biobots” with self-repair abilities.
• It highlights natural systems (embryos and regenerating animals) that reliably achieve correct anatomy despite disturbances.
• Computational neuroscience concepts—such as memory, prediction, and error‐correction—are proposed as tools to guide tissue formation.
• The authors suggest that bioelectric signals in non‐neural cells serve as a kind of “memory” that encodes a target shape, similar to how brains store memories.
• This top‐down approach may allow researchers to “program” tissue regeneration by correcting deviations from a stored target morphology.

Introduction: The Challenge and a New Approach

• Regenerative medicine aims to replace or repair organs that are damaged or missing, but simple cell assembly isn’t enough for complex 3D structures.
• Natural development and regeneration show that organisms can self‐organize into correct shapes even after perturbations.
• Traditional methods work from the bottom up (cell-by-cell), but this paper argues for a top‐down model—using high-level goal states or “target morphologies” to guide repair.
• Analogous to following a cooking recipe, the body “knows” step by step how to reassemble tissues into the desired shape.

Harnessing Non-Neural Bioelectricity for Organ-Level Programming

• Bioelectricity Explained: Cells use electrical signals (voltage differences across their membranes) to communicate—much like batteries power devices.
• Ion channels, pumps, and gap junctions create patterns of voltage that act as signals to guide cell behavior (proliferation, movement, and differentiation).
• Modern tools such as voltage-sensitive dyes and optogenetics let scientists measure and alter these signals.
• This bioelectrical “code” forms prepatterns in tissues, instructing cells on where and when to form specific organs.
• Think of it as a conductor (the bioelectric signal) leading an orchestra (the cells) to create a harmonious final structure.

A Top-Down Perspective on Pattern Control

• Instead of building a structure cell-by-cell (bottom-up), the top-down approach defines a final target shape or “memory” of the ideal organ.
• Cells compare their current state with this target, then adjust their behavior to reduce the difference—similar to a thermostat correcting room temperature.
• Concepts from computational neuroscience, such as the Free Energy Principle and active inference, are used to model this error-correction process.
• This process is like following a step-by-step recipe, where each step is monitored and corrected until the final desired shape is achieved.
• Feedback loops (error signals) ensure that once the target morphology is reached, cell activity ceases, preventing overgrowth.

Broader Implications: Parallels Between Neural Processing and Tissue Patterning

• Many of the same molecules (ion channels, gap junction proteins, neurotransmitters) are found both in the brain and in non-neural tissues.
• This suggests that non-neural tissues can process information and “remember” patterns much like neural circuits.
• Neural inputs (such as nerves) are known to affect regeneration, reinforcing the idea that electrical signals guide both brain function and organ patterning.
• These parallels open up new strategies for regenerative medicine—by targeting bioelectric circuits, one might control or reprogram organ formation.

Conclusions and Future Directions

• The study proposes that bioelectric signals encode a memory of the correct anatomical shape, guiding regeneration in a top-down manner.
• This method could overcome the limitations of bottom-up approaches that require micromanagement of countless molecular details.
• Future research should focus on “cracking” the bioelectric code to reliably program tissue repair and regeneration.
• Such breakthroughs may impact not only regenerative medicine but also areas like cancer treatment and synthetic bioengineering.

Appendix and Additional Concepts

• The paper also reviews computational models and control theories (e.g., predictive coding, active inference, and the Free Energy Principle) that explain how cells might “learn” their target morphology.
• These models provide a framework for understanding how global anatomical patterns can emerge from the coordinated activity of many cells.
• The integration of these high-level concepts with molecular biology offers a promising toolbox for future biomedical applications.

Key Takeaways

• Bioelectric signals in non-neural cells play a crucial role in orchestrating large-scale tissue patterning and regeneration.
• A top-down, computational neuroscience approach treats the desired organ shape as a target memory that cells work to achieve.
• This perspective opens up new avenues for regenerative medicine, enabling control over complex anatomical structures.
• Understanding and manipulating the bioelectric code may lead to advances in tissue repair, cancer suppression, and synthetic biology.

摘要概述

• 本文探讨了再生复杂器官（如肢体）以及制造具有自我修复能力的“生物机器人”的挑战。
• 文章强调了胚胎和再生动物等自然系统，即使受到干扰，也能可靠地形成正确的解剖结构。
• 作者提出，利用计算神经科学中的记忆、预测和误差校正等概念，可以指导组织形态的形成。
• 文中认为，非神经细胞中的生物电信号充当了一种“记忆”，储存着目标形态，就像大脑储存记忆一样。
• 这种自上而下的方法可能允许研究人员通过校正偏差来“编程”组织再生，达到预设的目标形态。

引言：挑战与新方法

• 再生医学的目标是替换或修复受损或缺失的器官，但仅仅依靠细胞组装不足以构建复杂的三维结构。
• 自然发育和再生过程中，生物体能够自组织成正确的形态，即使受到干扰也能恢复正常。
• 传统方法主要从下而上（逐个细胞），而本文主张采用自上而下的模型，即用高层次的目标状态或“目标形态”来指导修复。
• 这种方法类似于遵循烹饪食谱，身体一步步地“知道”如何重组组织以形成预期的形状。

利用非神经生物电实现器官级编程

• 生物电的解释：细胞利用膜电位差传递电信号，就像电池为设备供电一样。
• 离子通道、泵和缝隙连接共同产生电压模式，这些信号指导细胞行为（增殖、运动和分化）。
• 利用电压敏感染料和光遗传学等现代工具，科学家可以测量和操控这些电信号。
• 这种生物电“编码”在组织中形成预先设定的模式，指导细胞在何处何时构建特定器官。
• 可以将其比作指挥家（生物电信号）指挥乐队（细胞），最终奏响和谐的结构乐章。

自上而下的形态控制视角

• 与从细胞逐步构建结构的“自下而上”方法不同，自上而下的方法先定义一个最终的目标形态或“记忆”。
• 细胞通过比较当前状态与这一目标，调整其行为以减少差异，就像温控器调节室温一样。
• 文中利用计算神经科学中的概念，如自由能原理和主动推理，来模拟这种误差校正过程。
• 这一过程类似于按照食谱一步步操作，直到达到理想的最终形状。
• 反馈回路（误差信号）确保一旦达到目标形态，细胞活动就会停止，防止过度生长。

更广泛的启示：神经处理与组织形态调控之间的相似性

• 许多分子（如离子通道、缝隙连接蛋白和神经递质）在大脑和非神经组织中均有发现。
• 这表明非神经组织也能处理信息并“记住”形态，就像神经回路一样。
• 神经输入（如神经元的信号）已知会影响再生，这进一步支持了电信号在大脑功能和器官形态调控中的作用。
• 这一视角为再生医学开辟了新途径，通过针对生物电回路可以控制或重新编程器官形成。

结论与未来展望

• 本文提出生物电信号储存了正确解剖形态的记忆，通过自上而下的方式指导再生。
• 这种方法有望克服仅依赖自下而上微观分子调控的局限性。
• 未来研究应致力于“破解”生物电代码，以便可靠地编程组织修复和再生。
• 这种突破不仅会影响再生医学，还可能对癌症治疗和合成生物工程产生深远影响。

附录及其他概念

• 文章还回顾了计算模型和控制理论（如预测编码、主动推理和自由能原理），解释了细胞如何“学习”目标形态。
• 这些模型为理解全局解剖模式如何从众多细胞的协调活动中涌现提供了理论框架。
• 将这些高层概念与分子生物学结合，为未来生物医学应用提供了有前景的工具箱。

主要收获

• 非神经细胞中的生物电信号在协调大规模组织模式和再生中起着至关重要的作用。
• 利用计算神经科学的方法，可以将期望的器官形态视为一种目标记忆，驱动细胞向目标形态靠拢。
• 这种视角为再生医学提供了新思路，有望实现对复杂解剖结构的精确控制。
• 理解和操控生物电代码将推动组织修复、癌症抑制和合成生物学的发展。