用细胞衰老图谱量化衰老的耗散过程，揭示基因表达动态变化规律。
The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map
- 基于动力系统理论构建细胞衰老映射，以熵变和嵌入发散表征老化
- 发现不同组织与细胞类型中存在非线性转变和基因嵌入空间的发散模式
- 适合生物信息学、衰老机制研究者，可提供分子层面的衰老度量工具
我们提出一种基于动力系统的衰老新理论，并开发数据驱动的计算方法，量化细胞层面的变化。通过遍历性理论分解衰老过程中的动态变化，表明衰老本质上是生物系统中由非保守力引起的耗散过程。为量化耗散动态，采用基于Transformer的机器学习算法分析基因表达数据，将年龄作为标记项评估其在嵌入空间中的表现。通过分析基因与年龄嵌入的动态变化，构建了细胞衰老地图（CAM），识别出多种组织和细胞类型中基因嵌入空间的发散、非线性转变及熵变特征。结果揭示衰老是一种耗散过程，并提出一个可在分子层面测量衰老变化的计算框架。
原文摘要 · Abstract (English)
We propose a new theory for aging based on dynamical systems and provide a data-driven computational method to quantify the changes at the cellular level. We use ergodic theory to decompose the dynamics of changes during aging and show that aging is fundamentally a dissipative process within biological systems, akin to dynamical systems where dissipation occurs due to non-conservative forces. To quantify the dissipation dynamics, we employ a transformer-based machine learning algorithm to analyze gene expression data, incorporating age as a token to assess how age-related dissipation is reflected in the embedding space. By evaluating the dynamics of gene and age embeddings, we provide a cellular aging map (CAM) and identify patterns indicative of divergence in gene embedding space, nonlinear transitions, and entropy variations during aging for various tissues and cell types. Our results provide a novel perspective on aging as a dissipative process and introduce a computational framework that enables measuring age-related changes with molecular resolution.
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