Introduction

• Significant knowledge gaps exist regarding the control of large-scale anatomical homeostasis (how organisms maintain their shape).
• Fundamental advances in biomedicine require understanding cellular decision-making, not just molecular mechanisms.
• Non-neural bioelectricity is a key medium for computation in cellular collectives.
• The goal is “cracking the bioelectric code”: mapping electrical patterns to genetic and anatomical outcomes. This could enable “electroceutical” approaches for various medical applications.
• Body tissues form electrical networks (like the brain) that make decisions about dynamic anatomy. We have ways to control this for pattern editing.

Anatomical Homeostasis and the Anatomical Compiler

• The long-term goal is an “anatomical compiler”: specify a desired anatomical structure, and the system generates stimuli to guide cells to build it. This would revolutionize medicine.
• Cells are not passive building blocks; they are highly competent, making decisions and solving problems.
• Embryonic development involves cells working together towards large anatomical goals. Differentiating cell type isn’t enough; 3D organization is crucial.
• The standard developmental biology paradigm (gene regulatory networks -> emergence) has a profound inverse problem: it’s hard to know what to change upstream to get a desired downstream effect.
• Current models often lack predictive power. Even in simple examples, the precise algorithms and stop conditions are unknown.
• Moving focus from hardware and focusing to also ask about the anatomical, native software modules within and asking how reprogramable the system.

Plasticity and Pattern Homeostasis

• Examples of biological plasticity are used. The axolotl regenerates limbs, etc., The planarian flatworms are a system to understand plasticity due it its high ability for regneration.
• Planarian regeneration: Each fragment “knows” what’s missing and regenerates it. They are “immortal” due to continuous regeneration.
• Human liver regeneration, deer antler regeneration, and fingertip regeneration in children show that regeneration is not limited to “lower” animals.
• Picasso tadpoles: Frog facial features rearrange to form a normal frog face, even when starting from abnormal positions. Genetics specifies error minimization, not a fixed path.
• Pattern Homeostasis model introduced a feedback loops from anatomical devation to the genome via electric.
• The model set-point which includes The process of “current read, compare to a set-point, action, repeat.” and this feedback includes not the current levels of parameters, but a fairly complex specification on layout of an antamoy.
• Bioelectricity as a mechanism, an explicit representation of the system exists of the future anatomy.

Bioelectric Signaling

• Bioelectricity is a key component of the “morphogenetic field” – a field of information that influences cells.
• It is stated, Bioelectricity is not a standalone: other things are involved including chemical and physical factors and gradients.
• This is a very convinent compuational layer used, not accident, by evolution for decision-making.
• All cells have ion channels and many are electrically connected via gap junctions (electrical synapses), similar to the brain.
• “Neural decoding” in neuroscience tries to infer the informational content from electrical activity. The same principles can apply to other tissues.
• Voltage-sensitive dyes map electrical, simulating by computer is done by building models with cells, gap, and channels.
• Early from embrionic face developement has a bioelect patten as a prepattern on where organs go, this pre-pattern then influnces gene expression.
• An examle is also made on the bioelectric expression of tumuors and an example of diagnostics.
• Membrane potentials can influence.

Modulating Bioelectric Circuits

• Tools have been developed to manipulate bioelectric signaling: Changing connectivity by opening/closing gap junctions. Setting cell voltage by controlling ion channels (including optogenetic channels). These are molecular, not external electrical field, interventions.
• Induced voltage in areas to make modular “cascade sub-routines” making a voltage that is inductive and kickstarting body formation (an example is ectopic eyes, made using electirc signal).
• Standing bioelectric patterns are manipulated to make large anatomical change: This manipulation creates planeria that regrow and show it has the memory of its biolectricity even if cut, or having the planeria regrow into a similar yet differnt head species.
• An example of medicine use is regrowing frog limbs using this voltage trick with chemicals and bioelectric to influence modularity of limbs.
• There is a spinoff, mophaceuticals inc, doing this.
• Many mechansims between biolectricty and gene expresion in single cells.

Modeling and Control

• Multi-scale models combine single-cell bioelectric and transcriptional circuits with tissue-level dynamics. This helps understand the algorithms for anatomical control.
• These models predict experiments, allow virtual experiments, and are amenable to machine learning. This facilitates finding specific interventions.
• Focuses not on specific individual cell voltage, but its gradient among neightbors.
• Evolution uses different methods of voltages based on convenient factors.

Encoding Metaphor and Memory

• DNA sets cellular hardware (ion channels, etc.), but bioelectric states represent a kind of “software” due to post-translational modification.
• The bi-electric patten can persist even when removed the organs, creating a future set point in time and space to regrow into.
• There’s long-term “bi-electrical” memory, which is seen downstream in the long-term via change of structure and shape.

Towards Electroceuticals and Beyond

• With ion and cells and votlages we get “comptuation model for predicting outcomes.
• Examples include frog brian, planaria bi-stability, regeneration, and tumor regulation.

Toward Therapudic Platforms

• Computational models can predict ion channel combinations to shift bioelectric patterns towards health. A platform is being developed.

Key Conclusions

• A bioelectric computational layer sits between genotype and anatomy, making crucial decisions.
• Evolution likely used electrical signaling for computation very early.
• Understanding this bioelectric “language” is crucial for controlling collective cellular behavior and has broad implications.
• Anatomical Homostasis by cells and feedback, making bioelecticity an early computation level used.

引言

• 关于大尺度解剖稳态（生物体如何维持其形状）的控制，存在着重要的知识空白。
• 生物医学的根本进步需要理解细胞的决策过程，而不仅仅是分子机制。
• 非神经生物电是细胞群体计算的关键媒介。
• 目标是“破解生物电密码”：将电模式映射到基因和解剖结果。这可以为各种医疗应用启用“电疗”方法。
• 身体组织形成电网络（像大脑一样），对动态解剖结构做出决策。 我们有方法控制这种模式编辑。

解剖稳态与解剖编译器

• 长期目标是“解剖编译器”：指定所需的解剖结构，系统会生成刺激来引导细胞构建它。这将彻底改变医学。
• 细胞不是被动的构建块；它们具有高度的能力，可以做出决策并解决问题。
• 胚胎发育涉及细胞共同努力实现大型解剖目标。 区分细胞类型是不够的；三维组织至关重要。
• 标准发育生物学范式（基因调控网络 -> 涌现）存在一个深刻的反问题：很难知道要改变上游什么才能获得所需的下游效应。
• 当前的模型通常缺乏预测能力。 即使在简单的例子中，精确的算法和停止条件也是未知的。
• 将重点从硬件转移，并同时关注内部的解剖学、原生软件模块，并询问系统的可重编程性。

可塑性与模式稳态

• 使用了生物可塑性的例子。 蝾螈再生四肢等。 平板涡虫是一种了解可塑性的系统，因为它具有很高的再生能力。
• 涡虫再生：每个片段都“知道”缺少什么并再生它。 由于持续再生，它们是“不朽的”。
• 人类肝脏再生、鹿茸再生和儿童指尖再生表明，再生不仅限于“低等”动物。
• 毕加索蝌蚪：青蛙的面部特征重新排列形成正常的青蛙脸，即使从不正常的位置开始也是如此。 遗传学指定了错误最小化，而不是固定路径。
• 模式稳态模型通过电引入了解剖偏差到基因组的反馈回路。
• 模型设定点包括“当前读取、与设定点比较、动作、重复”的过程。该反馈不仅包括参数的当前水平，还包括对解剖布局的相当复杂的规范。
• 生物电作为一种机制，系统对未来解剖结构的明确表示。

生物电信号传导

• 生物电是“形态发生场”的关键组成部分——影响细胞的信息场。
• 据指出，生物电不是独立的：涉及其他因素，包括化学和物理因素和梯度。
• 这是一个进化用于决策的非常方便的计算层，而非偶然。
• 所有细胞都有离子通道，许多细胞通过间隙连接（电突触）电连接，类似于大脑。
• 神经科学中的“神经解码”试图从电活动中推断信息内容。 同样的原理也可以应用于其他组织。
• 电压敏感染料映射电，通过使用细胞、间隙和通道构建模型来用计算机模拟。
• 从早期胚胎面部发育开始，就有一个生物电模式作为器官去向的预模式，这个预模式随后会影响基因表达。
• 还举了一个关于肿瘤生物电表达和诊断的例子。
• 膜电位可以影响。

调节生物电回路

• 已经开发出操纵生物电信号的工具： 通过打开/关闭间隙连接来改变连接性。 通过控制离子通道（包括光遗传学通道）来设置细胞电压。 这些是分子干预，而不是外部电场干预。
• 在区域中诱导电压以产生模块化的“级联子程序”，从而产生具有诱导性和启动身体形成的电压（一个例子是异位眼睛，使用电信号制造）。
• 操纵稳定的生物电模式以进行大的解剖改变：这种操纵创造了涡虫，使其再生并显示即使被切割也具有其生物电的记忆，或者让涡虫再生为类似但不同的头部物种。
• 医学用途的一个例子是使用这种电压技巧与化学物质和生物电来影响四肢的模块化，从而再生青蛙四肢。
• 有一家衍生公司，mophaceuticals inc，正在这样做。
• 单细胞中生物电和基因表达之间的许多机制。

建模与控制

• 多尺度模型将单细胞生物电和转录回路与组织水平的动力学相结合。 这有助于理解解剖控制的算法。
• 这些模型可以预测实验，允许进行虚拟实验，并且适合机器学习。 这有助于找到具体的干预措施。
• 关注的不是特定单个细胞的电压，而是它在邻居之间的梯度。
• 进化根据方便的因素使用不同的电压方法。

编码隐喻和记忆

• DNA 设置细胞硬件（离子通道等），但生物电状态由于翻译后修饰而代表一种“软件”。
• 即使移除器官，生物电模式也可以持续存在，从而在时间和空间上创建一个未来的设定点以重新生长。
• 存在长期的“生物电”记忆，这可以通过结构和形状的变化在长期下游看到。

迈向电疗及其他

• 有了离子、细胞和电压，我们得到了“预测结果的计算模型”。
• 例子包括青蛙大脑、涡虫双稳态、再生和肿瘤调节。

迈向治疗平台

• 计算模型可以预测离子通道组合，以将生物电模式转向健康。 正在开发一个平台。

关键结论

• 一个生物电计算层位于基因型和解剖结构之间，做出关键决策。
• 进化很可能很早就使用电信号进行计算。
• 理解这种生物电“语言”对于控制集体细胞行为至关重要，并且具有广泛的意义。
• 细胞的解剖稳态和反馈，使生物电成为早期使用的计算水平。