A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915466172170240 |
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| author | Wang, Aoqi Liu, Jiajia Wen, Jianguo Luo, Yangyang Fan, Zhiwei Yang, Liren Hu, Xi Luo, Ruihan Yu, Yankai Li, Sophia Zhao, Weiling Zhou, Xiaobo |
| author_facet | Wang, Aoqi Liu, Jiajia Wen, Jianguo Luo, Yangyang Fan, Zhiwei Yang, Liren Hu, Xi Luo, Ruihan Yu, Yankai Li, Sophia Zhao, Weiling Zhou, Xiaobo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19800 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent Wang, Aoqi Liu, Jiajia Wen, Jianguo Luo, Yangyang Fan, Zhiwei Yang, Liren Hu, Xi Luo, Ruihan Yu, Yankai Li, Sophia Zhao, Weiling Zhou, Xiaobo Tissues and Organs Biomolecules 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. |
| title | A Multi-Layered Framework for Modeling Human Biology: From Basic AI Agents to a Full-Body AI Agent |
| topic | Tissues and Organs Biomolecules |
| url | https://arxiv.org/abs/2508.19800 |