Artificial Intelligence for Aging Control: Modeling, Predicting, and Steering Cellular Lifespan
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Barack Ndenga
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Abstract
Aging is traditionally characterized as an irreversible biological process driven by the cumulative accrual of molecular damage. In this article, I propose and articulate a fundamentally distinct conceptual framework: aging as a dynamical, information-driven process that can be modeled, predicted, and actively steered using artificial intelligence (AI). I position AI not as a passive analytical tool but as an enabling core technology capable of transforming aging into a controllable biological trajectory. By integrating multi-omic and multi-scale biological data, AI enables the precise optimization of the timing, sequencing, and personalization of therapeutic interventions, thereby reframing longevity science as a problem of temporal systems control. This perspective establishes a new theoretical foundation for precision geromedicine and next-generation longevity biotechnology.
Keywords: Artificial Intelligence in Aging,Aging as a Dynamical System,Closed-Loop Control Systems,Predictive Gerontology,Biological Aging Clocks,Precision Geromedicine,Longevity Biotechnology,Temporal Systems Control,AI-Driven Interventions,Multi-Scale Modeling,Personalized Aging Trajectories,Senotherapeutics,Cellular Reprogramming,Healthspan Extension,Systems Biology of Aging
Description
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.
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Except where otherwised noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States