论生长与形态，及功能：可复用的调控手柄控制表型变异

中文摘要 AI 辅助

表型转化如何通过底层调控动态的变化来实现，仍是发育生物学的核心问题。受达西·汤普森1917年《论生长与形态》的启发，我们探究连贯的大规模形态转化能否被编码为自组织发育系统的低维调制。我们使用神经细胞自动机（NCA）作为分布式发育的仿生模型，其中共享的局部调控网络从单个细胞生长出目标形态。我们对预训练的NCA应用低秩适应（LoRA），将每个适应的发育程序表示为固定调控支架的低秩调制。一个完全生长的二维表情符号表型的水平与垂直缩放均可通过秩一适应实现。它们的线性组合参数化地控制表型大小，泛化到训练分布之外，并与目标特异性适配器组合。引人注目的是，为一个表型学习的适应器可零样本迁移到共享同一参考支架的结构与语义多样的表型，同时基本保留内部特征。这表明存在可复用的系统级尺度超方向，而非形态特异性转化。从约25,000个独立训练的共享支架的表型特异性NCA适配器中，我们进一步识别出潜在的低维方向，其功能性地控制表型变异，包括缩放、风格和对称分裂。综上，我们的结果在二维NCA中实现了达西·汤普森非凡网格变换的计算实现——一个最小控制论组织，其中完全生长的表情符号表型的变异可通过调控权重空间中的低维方向进行编码、组合和控制。

英文摘要

How phenotypic transformations are implemented by changes in underlying regulatory dynamics remains a central question in developmental biology. Inspired by D'Arcy Thompson's 1917 "On Growth and Form", we ask whether coherent large-scale transformations of morphology can be encoded as low-dimensional modulations of a self-organizing developmental system. We use neural cellular automata (NCAs) as bio-inspired models of distributed development, in which a shared local regulatory network grows target morphologies from a single cell. We apply low-rank adaptation (LoRA) to pretrained NCAs, representing each adapted developmental program as a low-rank modulation of a fixed regulatory scaffold. Horizontal and vertical scaling of a fully grown 2D emoji phenotype can each be implemented by rank-one adaptations. Their linear combinations parametrically control phenotype size, generalize beyond the training distribution, and compose with target-specific adapters. Strikingly, adaptations learned for one phenotype transfer zero-shot across structurally and semantically diverse phenotypes sharing the same reference scaffold, while largely preserving internal features. This suggests reusable system-level hyper-directions of scale rather than morphology-specific transformations. From approximately 25,000 independently trained phenotype-specific NCA adapters with a shared scaffold, we further identify latent low-dimensional directions that functionally control phenotypic variation including scaling, style, and symmetrical fission. Together, our results provide a computational realization of D'Arcy Thompson's remarkable grid transformations in a 2D NCA---a minimal cybernetic tissue in which variations of fully grown emoji phenotypes can be encoded, combined, and controlled through low-dimensional directions in regulatory weight space.

发表机构

• Allen Discovery Center at Tufts University（塔夫茨大学艾伦发现中心）
• IT University of Copenhagen（哥本哈根信息技术大学）
• Sakana AI（Sakana人工智能公司）
• Wyss Institute for Biologically Inspired Engineering at Harvard University（哈佛大学怀斯生物启发工程研究所）

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