Regeneration

• Regeneration is the ability to regrow lost body parts; it’s distributed unevenly across the tree of life, not simply “simple” vs. “advanced” organisms.
• Examples include Planaria (whole body), salamanders (limbs, organs), deer (antlers), and humans (liver).
• Regeneration is *not* inherently linked to increased cancer risk; good regenerators often have *low* cancer rates, suggesting strong anatomical control.
• Levin proposes regeneration is a fundamental aspect of *anatomical homeostasis* – living things solving problems in “morphospace” to reach a target morphology.
• Embryonic development can be viewed as a series of regenerative events, each stage “correcting” the previous one towards the final form.
• A bioelectrical pattern (discussed later) may store the “target morphology” and drive the regenerative process.
• Challenges for regeneration in land animals (vs. aquatic) include dry air, mechanical stress on wounds, and faster life cycles; scarring may be a trade-off.
• Biodomes (wearable bioreactors) with drug cocktails can trigger limb regeneration in frogs (Xenopus) after a 24-hour application; this suggests high-level control, not micromanagement.
• This one trial shows massive promise of regeneration, because it was successful with their *first try* implying that many combinations will exist for regenerative cues.

Bioelectricity

• Multicellularity requires cells to work together towards large-scale goals, beyond individual cell capabilities.
• Cells need a communication mechanism (information structure) and a way to store the “set point” (target morphology). Thermostats serve a great example.
• Traditional molecular biology often focuses on forward emergence (genes expressing, leading to an outcome) but does have feedback loops.
• Levin highlights feedback loops and problem-solving: organisms often reach the “correct” outcome despite perturbations (e.g., extra or missing cells).
• Electrical networks (like in the brain) provide a computational medium for collective intelligence, coordinating cell activity.
• This capability predates brains; it exists in bacteria and unicellular ancestors, highlighting ancient origins.
• Evolution repurposed bioelectric networks: from controlling behavior in 3D space (brains) to navigating morphospace (embryonic development) to physiological space (single cells).
• Most neuroscience principles apply *outside* the nervous system; neurons and non-neural cells share similar bioelectric mechanisms.
• Cells use ion channels (voltage gradients), gap junctions (electrical synapses), and neurotransmitters for bioelectric communication.
• Researchers can “read and write” this electrical information using voltage-sensitive dyes and by manipulating ion channels/gap junctions (no external fields).
• Key ions include chloride, protons, potassium, and sodium; the spatial pattern of voltage gradients, not the specific ions, is often the crucial signal.
• Voltage, a “macrostate”, can be achieved through many different ion concentration “microstates,” highlighting high-level control possibilities.
• Tools include voltage-sensitive fluorescent dyes, genetically encoded voltage reporters, and methods to manipulate ion channels/gap junctions (pharmacology, mutations, optogenetics).

Planaria and Barium Adaptation

• Planaria exposed to barium (a potassium channel blocker) initially experience head degradation, but can regenerate barium-resistant heads.
• Transcriptomic analysis reveals a small number of genes enabling barium adaptation.
• Planaria never encounter barium in the wild, suggesting a *general* problem-solving ability, not a specific, evolved response.
• This adaptability illustrates cellular “intelligence”: using existing tools (transcriptional effectors) to solve novel physiological challenges.
• The memory of barium resistance is lost when returning to water, suggesting either energetic cost or instability of the adapted transcriptional state.
• a two-headed phenotype created, where the electrical “memory” stores what would-be the normal configuration.
• This highlights non-genetic cellular problem-solving.

Xenobots (Synthetic Living Machines)

• Xenobots are created by isolating frog skin cells from the normal embryonic context.
• Isolated cells *spontaneously* form structures with novel behaviors: movement, navigation, maze solving, damage regeneration, and even *kinematic self-replication* (building new xenobots from loose cells).
• This demonstrates inherent plasticity and problem-solving abilities of cells *without* external genetic manipulation.
• Xenobots challenge the notion that skin cells “naturally” want to be a two-dimensional layer; their behavior depends on context.
• Xenobot example highlights *collective behavior beyond the cells normal intended behavior*.
• Applications include useful synthetic machines (sensing, exploration, micro-sculpting organs) and in-body tasks (cleaning up joints, targeting cancer cells).
• Xenobots can be a platform for studying “scaling of goals”: how the collective’s goals emerge from individual cell goals, relevant for various complex systems.
• this challenges current definition of organic versus robotic or electronic, blurring boundaries.

Intelligence and Ethics

• Levin proposes intelligence as “the ability to get to the same goal by different means” (William James), applicable across various problem spaces (morphospace, transcriptional space, etc.).
• The “size” of the goals a system can pursue reflects its cognitive sophistication; bacteria have small, local goals, while humans can have large, abstract, long-term goals.
• Levin advocates a *gradual* view of intelligence, rejecting binary categories (humans/animals vs. “just physics”); all living systems have some degree of intelligence.
• We’re good at recognizing intelligence in familiar forms (medium-sized objects moving at medium speeds) but poor at recognizing unconventional intelligence.
• Xenobots (and future bioengineered beings) challenge ethical assumptions based on origin (evolved vs. designed) and composition (organic vs. synthetic).
• We may encounter/create many kinds of intelligences: organic, cybernetic, mixed, and other types of intelligences we can hardly imagine..
• Ethics must focus on *cognitive capacity*, not origin or composition; the key is how we relate to diverse intelligences, regardless of their appearance or origin.
• The future of humans may involve extensive bodily modifications, making genetics less relevant; the defining feature may be the capacity for moral concern for others.
• Future medicine may shift from “hardware” (genes, proteins) to “software” (higher-level control structures), motivating systems to reach healthy states rather than just suppressing symptoms.
• Training, rather than micromanagement, may be key. As evident from other portions of the talk, Levin aims for macro level biological changes that allow self-organization.
• Interdisciplinary thinking is crucial. Scientists need to be exposed to several other types of thought-paths such as physisict, engineers, computer scientisits, etc..

再生

• 再生是重新生长失去的身体部位的能力；它在生命之树上分布不均，并非简单地分为“简单”生物和“高级”生物。
• 例子包括涡虫（全身）、蝾螈（四肢、器官）、鹿（鹿角）和人类（肝脏）。
• 再生*不*与癌症风险增加有内在联系；良好的再生者通常具有*低*癌症发生率，表明强大的解剖控制能力。
• 莱文提出再生是*解剖稳态*的一个基本方面——生物体在“形态空间”中解决问题以达到目标形态。
• 胚胎发育可以被视为一系列再生事件，每个阶段都将前一个阶段“纠正”为最终形态。
• 生物电模式（稍后讨论）可能存储“目标形态”并驱动再生过程。
• 陆地动物（相对于水生动物）再生的挑战包括干燥空气、伤口上的机械应力以及更快的生命周期；疤痕形成可能是一种权衡。
• 带有药物混合物的生物穹顶（可穿戴生物反应器）可以在24小时应用后触发青蛙（非洲爪蟾）的肢体再生；这表明是高级控制，而不是微观管理。
• 这项试验显示了再生的巨大希望，因为它是*首次尝试*就成功了，这意味着存在许多再生线索的组合。

生物电

• 多细胞性要求细胞朝着超越单个细胞能力的大规模目标协同工作。
• 细胞需要一种通信机制（信息结构）和一种存储“设定点”（目标形态）的方法。恒温器就是一个很好的例子。
• 传统的分子生物学通常侧重于前向涌现（基因表达，导致结果），但确实有反馈回路。
• 莱文强调反馈回路和问题解决：尽管受到扰动（例如，额外或缺失的细胞），生物体通常会达到“正确”的结果。
• 电网络（如大脑中的）为集体智能提供了一种计算媒介，协调细胞活动。
• 这种能力早于大脑；它存在于细菌和单细胞祖先中，突出了古老的起源。
• 进化重新利用了生物电网络：从控制3D空间中的行为（大脑）到导航形态空间（胚胎发育）再到生理空间（单细胞）。
• 大多数神经科学原理都适用于神经系统*之外*；神经元和非神经细胞共享相似的生物电机制。
• 细胞使用离子通道（电压梯度）、间隙连接（电突触）和神经递质进行生物电通信。
• 研究人员可以使用电压敏感染料并通过操纵离子通道/间隙连接（无需外部场）来“读取和写入”这些电信息。
• 关键离子包括氯离子、质子、钾离子和钠离子；电压梯度的空间模式，而不是特定的离子，通常是关键信号。
• 电压，一种“宏观状态”，可以通过许多不同的离子浓度“微观状态”来实现，突出了高级控制的可能性。
• 工具包括电压敏感荧光染料、基因编码的电压报告器，以及操纵离子通道/间隙连接的方法（药理学、突变、光遗传学）。

涡虫和钡适应

• 暴露于钡（一种钾通道阻滞剂）的涡虫最初会经历头部退化，但可以再生耐钡的头部。
• 转录组分析揭示了少数使钡适应的基因。
• 涡虫在野外从未遇到过钡，这表明它是一种*普遍的*解决问题的能力，而不是一种特定的、进化的反应。
• 这种适应性说明了细胞的“智能”：使用现有工具（转录效应器）来解决新的生理挑战。
• 当返回水中时，钡抗性的记忆会丧失，表明适应的转录状态要么存在能量成本，要么不稳定。
• 创建了一个双头表型，其中电“记忆”存储了正常的配置。
• 这突出了非遗传细胞解决问题的能力。

异种机器人（合成活体机器）

• 异种机器人是通过将青蛙皮肤细胞与正常的胚胎环境隔离而产生的。
• 分离的细胞*自发地*形成具有新行为的结构：运动、导航、迷宫求解、损伤再生，甚至*运动学自我复制*（从松散细胞中构建新的异种机器人）。
• 这表明了细胞固有的可塑性和解决问题的能力，而*无需*外部基因操作。
• 异种机器人挑战了皮肤细胞“自然”想要成为二维层的观念；它们的行为取决于环境。
• 异种机器人例子突出了*超越细胞正常预期行为的集体行为*。
• 应用包括有用的合成机器（传感、探索、微雕刻器官）和体内任务（清理关节、靶向癌细胞）。
• 异种机器人可以成为研究“目标尺度”的平台：集体的目标如何从单个细胞的目标中产生，这与各种复杂系统相关。
• 这挑战了当前对有机体与机器人或电子设备的定义，模糊了边界。

智能与伦理

• 莱文将智能定义为“通过不同方式达到相同目标的能力”（威廉·詹姆斯），适用于各种问题空间（形态空间、转录空间等）。
• 系统可以追求的目标的“大小”反映了其认知的复杂程度；细菌有小的、局部的目标，而人类可以有大的、抽象的、长期的目标。
• 莱文主张对智能采取*渐进的*观点，拒绝二元分类（人类/动物与“仅仅是物理学”）；所有生命系统都有一定程度的智能。
• 我们擅长识别熟悉形式的智能（中等大小的物体以中等速度移动），但难以识别非常规的智能。
• 异种机器人（以及未来的生物工程生物）挑战了基于起源（进化与设计）和组成（有机与合成）的伦理假设。
• 我们可能会遇到/创造多种类型的智能：有机的、控制论的、混合的，以及我们几乎无法想象的其他类型的智能。
• 伦理必须关注*认知能力*，而不是起源或组成；关键在于我们如何与不同的智能联系，无论它们的外观或起源如何。
• 人类的未来可能涉及广泛的身体改造，使遗传学变得不那么重要；决定性的特征可能是对他人道德关怀的能力。
• 未来的医学可能会从“硬件”（基因、蛋白质）转向“软件”（更高级别的控制结构），激励系统达到健康状态，而不仅仅是抑制症状。
• 训练，而不是微观管理，可能是关键。 正如谈话的其他部分所证明的那样，Levin的目标是宏观层面的生物变化，以允许自组织。
• 跨学科思考至关重要。 科学家需要接触其他类型的思维路径，例如物理学家、工程师、计算机科学家等。