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

Area of Science:

• Computational Biology
• Systems Biology
• Artificial Intelligence in Medicine

Background:

• Understanding complex human physiological and pathological processes requires multi-level biological integration.
• Current models often lack the ability to connect molecular changes to systemic outcomes.
• Predictive modeling for disease progression and therapeutic response remains a significant challenge.

Purpose of the Study:

• To introduce the Full-Body AI Agent, a comprehensive system for simulating, analyzing, and optimizing human body dynamics.
• To demonstrate the framework's utility through specialized agents for metastasis and drug development.
• To advance understanding of disease mechanisms and support personalized medicine.

Main Methods:

• Integration of computational models, machine learning, and experimental platforms.
• Multi-scale analysis from molecules to entire body systems.
• Development of specialized AI agents for metastasis scoring and system-level drug development.

Main Results:

• The metastasis AI Agent characterizes tumor progression by integrating molecular, cellular, and systemic signals.
• The drug AI Agent guides preclinical evaluations with full-body physiological constraints for predictive efficacy and toxicity.
• Demonstrated potential for cross-scale reasoning to address complex biomedical challenges.

Conclusions:

• The Full-Body AI Agent framework enables integrated, multi-level analysis of biological systems.
• Specialized agents show promise in advancing cancer research and drug development.
• This approach enhances predictive modeling for improved healthcare outcomes.