Introduction: Planarian Regeneration as a Model of Anatomical Homeostasis

• Planarians are flatworms with an extraordinary ability to regenerate lost parts. Any fragment can regrow a fully formed, properly proportioned body.
• Their regeneration process maintains anatomical homeostasis – the overall body plan remains correct even as cells are replaced.
• This paper explores how regeneration is controlled not only by genes but also by bioelectric signals and computational networks.

Functional Features of Planarians

• They have complex organ systems, a true brain, and diverse sensory systems that detect chemicals, gravity, and even weak radiation.
• Every piece of the planarian contains a built-in “target morphology” – instructions to rebuild the missing head or tail.
• Regeneration is rapid (often within a week) and maintains proper scaling whether the animal grows or shrinks.

Key Puzzles and Knowledge Gaps

• How does a wound decide whether to form a head or a tail when adjacent cells originally had the same information?
• Despite accumulating many mutations over time, planarians always regenerate perfectly – suggesting control mechanisms beyond genetic code.
• There are no stable mutant lines with abnormal body plans, hinting that regeneration is governed by additional layers of control.
• A thought experiment: If regenerative stem cells (neoblasts) from two species with different head shapes were mixed, what head would form? This shows our lack of predictive models.

Physiological Controls of Patterning

• Bioelectric Signals:
  • Cells maintain a membrane potential (voltage across their membranes) using ion channels and pumps. Think of this as each cell’s battery.
  • Gap junctions are tiny channels that let neighboring cells share electrical information, like wires connecting parts of a circuit.
• Prediction 1: Ion channels and voltage gradients are key in determining head-tail formation.
  • Altering these electrical gradients can lead to abnormalities like double-headed or headless animals.
• Prediction 2: Neurotransmitters, usually known for nerve signals, also affect regeneration.
  • They act as morphogens – substances that provide cells with positional clues, similar to a color gradient that shows a map.
• Prediction 3: The final anatomical outcome can diverge from the genetic “default.”
  • Bioelectric circuits can override genetic instructions, resulting in alternative, stable outcomes (for example, a different head shape).
• Prediction 4: Pattern memory – the stored information of the desired body plan – can be rewritten.
  • Short-term treatments that change bioelectric signals can permanently reset the regeneration target, much like rewriting data in a computer.

Computational Approaches to Understanding Regeneration

• Models based on reaction-diffusion use chemical gradients (morphogen gradients) to provide cells with positional information.
  • Analogy: Like a drop of dye diffusing in water to create a color gradient, these chemical signals help cells “read” their location.
• Advanced simulations integrate genetic, biochemical, and bioelectric data to predict how tissues decide on their final shape.
• Machine learning tools help reverse-engineer regulatory networks from experimental data, offering insights into the algorithms of regeneration.
• Challenges remain in scaling these models so they accurately predict outcomes in both whole organisms and small fragments.

Conclusion: Integrating Bioelectricity, Genetics, and Computation

• Planarian regeneration is controlled by both genetic instructions and bioelectric signals, which together set a “target morphology.”
• The concept of pattern memory suggests that tissues store information about the ideal body plan and can update it under certain conditions.
• Computational models (including reaction-diffusion and machine learning approaches) are essential for understanding how these signals are integrated to produce a coherent form.
• This research has important implications for regenerative medicine, morphogenetic engineering, and even robotics, as it reveals how decentralized decision-making can reliably rebuild complex structures.

Additional Key Points and Definitions

• Neoblasts: Regenerative stem cells in planarians that can develop into any cell type during regeneration.
• Bioelectricity: The natural electrical signals within and between cells; imagine it as the circuitry that guides how the body rebuilds itself.
• Morphogen Gradients: Gradual changes in the concentration of signaling chemicals that provide cells with a “map” of their position in the body.
• Homeostasis: The process by which organisms maintain a stable internal environment; similar to how a thermostat keeps room temperature steady.

Summary of Figures and Tables (from the Paper)

• Figures illustrate:
  • How polarity is re-scaled in fragments (like cutting a magnet and each piece forming its own north and south pole).
  • The role of bioelectric signaling in determining anatomical outcomes.
  • Computational models and databases that match experimental manipulations with regeneration outcomes.
• Tables list:
  • Cellular behaviors affected by bioelectric events (such as cell division, migration, and differentiation).
  • Experimental evidence connecting bioelectric signals to pattern formation.
  • Specific ion channels and pumps that have been implicated in regeneration across different species.

Overall Implications

• Regeneration is governed by complex feedback loops involving both electrical and chemical signals.
• This understanding may lead to new therapies for injuries and degenerative diseases by learning how to “reset” pattern memory.
• The interdisciplinary approach combining biology, physics, and computer science offers a new framework for designing self-repairing systems.

End of English Summary

引言：以蜉蝼再生为模型探讨解剖稳态

• 蜉蝼是一种扁虫，具有惊人的再生能力，任何一小部分都能长出一个完整且比例正确的身体。
• 它们的再生过程保持了解剖稳态——即使细胞不断更替，整体身体结构依然正确。
• 本文探讨了再生如何不仅依赖于基因，还受到生物电信号和计算模型的控制。

蜉蝼的基本功能特征

• 它们拥有复杂的器官系统、真正的大脑以及多种感官系统，能检测化学物质、重力，甚至微弱的辐射。
• 每一部分体内都包含“目标形态”的内在指令——指导重生出缺失的头部或尾部。
• 再生过程迅速（通常在一周内完成），并且无论生长或萎缩，都能保持正确的比例。

主要谜题与知识空白

• 当伤口处的相邻细胞原本拥有相同信息时，如何决定哪边长头哪边长尾？
• 尽管基因突变不断累积，蜉蝼依然总能完美再生，这表明再生控制机制超越了单纯的基因编码。
• 没有稳定的异常形态突变系，这提示着解剖形态的控制还涉及其他调控层次。
• 例如，一个思维实验：如果将来自不同物种（头部形态不同）的再生干细胞（新生细胞）混合，结果会长出怎样的头？这揭示了我们缺乏能够预测结果的模型。

形态模式控制的生理机制

• 生物电信号：
  • 细胞利用离子通道和泵维持跨膜电位，就像每个细胞都有一个电池。
  • 缝隙连接允许相邻细胞共享电信号，类似于电路中的连线。
• 预测1：离子通道和电压梯度在决定伤口生成头或尾中起关键作用。
  • 实验显示，改变这些电梯度可以导致双头或无头现象。
• 预测2：神经递质除了在神经传导中发挥作用外，也影响再生。
  • 它们充当形态原（提供位置信息的物质），类似于用渐变色标示地图。
• 预测3：最终的解剖结果可能偏离基因的“默认”状态。
  • 生物电回路可以覆盖基因指令，导致产生另一种稳定的形态（如不同的头部形状）。
• 预测4：形态记忆——即储存着理想身体形态的信息——是可以被重写的。
  • 短暂改变生物电信号的处理可以永久性地重置再生目标，就像重新写入电脑存储器一样。

计算模型对再生过程的理解

• 反应扩散模型利用化学梯度（形态原梯度）为细胞提供位置信息。
  • 类比：就像一滴染料在水中扩散形成渐变色一样，这些化学信号为细胞提供“位置图”。
• 先进的计算模拟整合了基因、化学及生物电数据，预测组织如何决定最终形态。
• 机器学习工具帮助从实验数据中推断调控网络，揭示再生背后的算法。
• 当前的挑战在于如何使模型在从整体到局部都能准确预测再生结果。

结论：生物电、基因与计算的整合

• 蜉蝼再生是由基因指令和生物电信号共同控制的，这两者共同设定了“目标形态”。
• 形态记忆的概念表明，组织中储存着理想体型的信息，并可在特定条件下被更新。
• 反应扩散和机器学习等计算模型对于理解这些信号如何整合以形成完整结构至关重要。
• 这一研究为再生医学、形态工程甚至机器人技术提供了新思路，展示了分布式决策如何可靠地重建复杂结构。

其他关键点与定义

• 新生细胞（Neoblasts）： 蜉蝼中的再生干细胞，能够分化成任何细胞类型，支持再生过程。
• 生物电： 细胞内外自然产生的电信号，可视为指导细胞如何重构自身的电路板。
• 形态原梯度： 随着化学物质浓度逐渐变化，为细胞提供位置与命运信息的“地图”。
• 稳态（Homeostasis）： 维持体内环境稳定的过程，就像恒温器调节室温一样。

论文图表概要

• 图示内容包括：
  • 如何在断片中重新建立极性（类似于切割磁铁后，每片重新形成南北极）。
  • 生物电信号如何指导解剖结果，以及改变这些信号如何导致再生异常。
  • 利用计算模型和数据库将实验操作与再生结果相对应的方式。
• 表格内容列出了：
  • 受生物电事件调控的细胞行为（如细胞增殖、迁移和分化）。
  • 证明生物电信号与形态形成之间关系的实验数据。
  • 在不同物种中，与再生相关的离子通道和泵的具体作用。

总体意义

• 再生过程由电和化学信号的复杂反馈回路共同调控。
• 理解这些机制有助于开发新的治疗方法，用于修复创伤和治疗退行性疾病，因为它揭示了如何“重置”形态记忆。
• 跨学科方法（结合生物学、物理学和计算科学）为设计自我修复系统提供了全新框架。

结束：中文总结完毕