Artificial Intelligence for Aging Control: Modeling, Predicting, and Steering Cellular Lifespan

This repository contains a curated collection of conceptual scientific figures accompanying the article “Artificial Intelligence for Aging Control: Modeling, Predicting, and Steering Cellular Lifespan”. The figures are designed to support a systems-level and information-driven framework of biological aging, positioning aging not as a passive accumulation of damage, but as a dynamic, modelable, and potentially steerable biological process. The visual materials illustrate key concepts developed in the article, including: Aging as a trajectory in high-dimensional biological state space Loss of biological function as a consequence of information degradation and increased biological noise The role of artificial intelligence in modeling, forecasting, and controlling aging dynamics Closed-loop AI–biology control systems for adaptive longevity interventions Temporal personalization and optimization of therapeutic strategies Integration of emerging longevity biotechnologies into AI-orchestrated platforms These figures are intended for use in scientific publications, preprints, presentations, and educational contexts, particularly within the fields of aging biology, systems biology, artificial intelligence in medicine, and longevity biotechnology. This repository does not present experimental datasets, but rather provides a conceptual and theoretical visualization framework aimed at facilitating interdisciplinary dialogue and accelerating the translation of aging research toward predictive and controllable models of biological time.