Khodaee Farhan, Zandie Rohola, Xia Yufan, Edelman Elazer R
Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, 02139, MA, USA.
Department of Medicine (Cardiovascular Medicine), Brigham and Women's Hospital, Boston, 02115, MA, USA.
ArXiv. 2025 Apr 17:arXiv:2504.13044v1.
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.
我们基于动力系统提出了一种新的衰老理论，并提供了一种数据驱动的计算方法来量化细胞水平的变化。我们使用遍历理论来分解衰老过程中变化的动力学，并表明衰老从根本上说是生物系统内的一个耗散过程，类似于由于非保守力而发生耗散的动力系统。为了量化耗散动力学，我们采用基于Transformer的机器学习算法来分析基因表达数据，将年龄作为一个标记纳入，以评估与年龄相关的耗散如何在嵌入空间中得到反映。通过评估基因和年龄嵌入的动力学，我们提供了一个细胞衰老图谱（CAM），并识别出在衰老过程中各种组织和细胞类型的基因嵌入空间中的差异、非线性转变和熵变化的指示模式。我们的结果为衰老作为一个耗散过程提供了一个新的视角，并引入了一个计算框架，能够以分子分辨率测量与年龄相关的变化。
