细胞发育遵循最小作用量路径
Cellular Development Follows the Path of Minimum Action
AI总结：
本文提出细胞发育遵循最小作用量路径，并基于最小作用量原理与最大熵的联系，利用 Transformer 框架量化单细胞 RNA 数据中的熵产生、曲率和不可逆性，揭示细胞命运决定背后的热力学与信息约束。
摘要：
细胞发育遵循一条随机但受规则支配的轨迹，但其深层原理仍不明确。在此，我们提出细胞发育遵循最小作用量路径，这与支配自然界动态系统的基础物理定律相一致。我们引入了一个计算框架，该框架利用最小作用量原理与最大熵之间的深层联系，使用 Transformer 架构对发育过程进行建模。该方法能够精确量化单细胞 RNA 序列数据中发育不对称性的熵产生、信息流曲率和局部不可逆性。在这个统一框架内，我们提供了可解释的度量指标：熵用于刻画探索-利用权衡，曲率用于评估可塑性-弹性动力学，熵产生用于表征去分化和转分化。我们在单细胞和胚胎发育数据集上验证了我们的方法，证明了其揭示塑造细胞命运决定的隐藏热力学和信息约束的能力。
英文摘要：
Cellular development follows a stochastic yet rule-governed trajectory, though the underlying principles remain elusive. Here, we propose that cellular development follows paths of least action, aligning with foundational physical laws that govern dynamic systems across nature. We introduce a computational framework that takes advantage of the deep connection between the principle of least action and maximum entropy to model developmental processes using Transformers architecture. This approach enables precise quantification of entropy production, information flow curvature, and local irreversibility for developmental asymmetry in single-cell RNA sequence data. Within this unified framework, we provide interpretable metrics: entropy to capture exploration-exploitation trade-offs, curvature to assess plasticity-elasticity dynamics, and entropy production to characterize dedifferentiation and transdifferentiation. We validate our method across both single-cell and embryonic development datasets, demonstrating its ability to reveal hidden thermodynamic and informational constraints shaping cellular fate decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
