A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent

TL;DR

This paper proposes a comprehensive multi-layered AI framework that models the human body across biological levels, integrating computational and experimental tools to improve disease understanding and therapeutic development.

Contribution

It introduces the Full-Body AI Agent framework, combining multi-scale models and two specialized agents for metastasis analysis and drug development, advancing holistic biomedical AI applications.

Findings

Demonstrated multi-scale metastasis scoring system.

Developed a system-level drug evaluation paradigm.

Showed potential for improved disease modeling and therapy prediction.

Abstract

We envision the Full-Body AI Agent as a comprehensive AI system designed to simulate, analyze, and optimize the dynamic processes of the human body across multiple biological levels. By integrating computational models, machine learning tools, and experimental platforms, this system aims to replicate and predict both physiological and pathological processes, ranging from molecules and cells to tissues, organs, and entire body systems. Central to the Full-Body AI Agent is its emphasis on integration and coordination across these biological levels, enabling analysis of how molecular changes influence cellular behaviors, tissue responses, organ function, and systemic outcomes. With a focus on biological functionality, the system is designed to advance the understanding of disease mechanisms, support the development of therapeutic interventions, and enhance personalized medicine. We propose two specialized implementations to demonstrate the utility of this framework: (1) the metastasis AI Agent, a multi-scale metastasis scoring system that characterizes tumor progression across the initiation, dissemination, and colonization phases by integrating molecular, cellular, and systemic signals; and (2) the drug AI Agent, a system-level drug development paradigm in which a drug AI-Agent dynamically guides preclinical evaluations, including organoids and chip-based models, by providing full-body physiological constraints. This approach enables the predictive modeling of long-term efficacy and toxicity beyond what localized models alone can achieve. These two agents illustrate the potential of Full-Body AI Agent to address complex biomedical challenges through multi-level integration and cross-scale reasoning.