The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map

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.

@article{khodaee2025_2504.13044,
  title={ The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map },
  author={ Farhan Khodaee and Rohola Zandie and Yufan Xia and Elazer R. Edelman },
  journal={arXiv preprint arXiv:2504.13044},
  year={ 2025 }
}