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

2025-04-17

AI Summary

Current understanding of cellular senescence lacks a dynamic, systems-level mechanistic explanation. Method: We propose a dissipative dynamics theory of aging, modeling senescence as a non-conservative, energy-dissipating process. Integrating ergodic theory with a Transformer architecture—where chronological age serves as the token—we construct a computable Cellular Aging Map (CAM) from multi-tissue gene expression data. This enables characterization of nonlinear gene–age trajectories, embedding-space divergence, and entropy evolution in a low-dimensional latent space. Contributions/Results: (1) We demonstrate that aging fundamentally reflects ergodicity degradation and entropy increase in cellular state space; (2) we quantitatively identify tissue-invariant critical transitions and embedding divergence signatures marking aging onset; (3) we derive novel, dynamically interpretable biomarkers grounded in dissipative dynamics, enabling mechanism-informed therapeutic targeting.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Machine Learning: Probabilistic Circuits and Graphical Models
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Agentic search
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

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.

Problem

• Proposes aging as a dissipative process in biological systems
• Quantifies cellular aging using transformer-based gene expression analysis
• Develops a computational framework to map age-related molecular changes

Innovation

• Dynamical systems theory for aging analysis
• Transformer-based algorithm for gene data
• Cellular aging map with molecular resolution