The Dissipation Theory of Aging: A Quantitative Analysis Using a Cellular Aging Map
This provides a novel perspective on aging as a dissipative process, potentially advancing biological aging research, though it appears incremental in applying existing ML methods to new data.
The authors tackled the problem of understanding aging by proposing a new theory based on dynamical systems and developing a computational method to quantify cellular-level changes, resulting in a cellular aging map that identifies patterns like divergence in gene embedding space and entropy variations during aging.
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.