The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913798304038912 |
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| author | Khodaee, Farhan Zandie, Rohola Xia, Yufan Edelman, Elazer R. |
| author_facet | Khodaee, Farhan Zandie, Rohola Xia, Yufan Edelman, Elazer R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13044 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map Khodaee, Farhan Zandie, Rohola Xia, Yufan Edelman, Elazer R. Quantitative Methods Machine Learning Biological Physics 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. |
| title | The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map |
| topic | Quantitative Methods Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2504.13044 |