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Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces
Marco Ruscone1, Miguel Vazquez2, Alfonso Valencia2,3
1Barcelona Supercomputing Center, Life Science Department, Barcelona, Spain. marco.ruscone@bsc.es.
NPJ Systems Biology and Applications
|June 15, 2026
Summary
This study introduces AI laboratory assistants using Large Language Model (LLM) agents and Model Context Protocol (MCP) servers for rapid mechanistic model prototyping. Researchers can now build complex biological models via natural language, accelerating computational hypothesis exploration.
Area of Science:
- Computational Biology
- Systems Biology
- Artificial Intelligence in Science
Background:
- Mechanistic modeling of multicellular systems is time-consuming and requires significant computational expertise.
- Current methods for building complex biological models are often inaccessible to researchers without specialized coding skills.
Purpose of the Study:
- To develop an AI-driven framework for rapid prototyping of multicellular mechanistic models.
- To enable researchers to construct and simulate complex biological models using natural language interactions.
Main Methods:
- Intelligent tool orchestration via Model Context Protocol (MCP) servers.
- Large Language Model (LLM) agents acting as AI laboratory assistants.
- Integration of NeKo (gene regulatory networks), MaBoSS (Boolean models), and PhysiCell (agent-based models) via MCP servers.
Main Results:
- A multiscale model of cancer cell fate was constructed entirely through natural language interactions.
- The workflow demonstrated AI-assisted model prototyping without manual coding or parameter editing.
- Key principles for biological AI-tool integration were identified, including tool granularity, session management, and flexible orchestration.
Conclusions:
- The proposed framework significantly accelerates the exploration of computational hypotheses in biology.
- AI agents can serve as effective laboratory assistants for building and simulating complex biological models.
- This approach lays the foundation for broader adoption of computational modeling in biological research through natural language interfaces.
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