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<p>Constructing multicellular mechanistic models traditionally requires extensive time and computational expertise. We introduce intelligent tool orchestration via Model Context Protocol (MCP) servers, enabling Large Language Model (LLM) agents to act as AI laboratory assistants for rapid model prototyping. We demonstrate this approach by constructing a multiscale model of cancer cell fate in response to TNF using an AI agent connected to MCP servers interfacing with three complementary tools: NeKo for gene regulatory networks construction, MaBoSS for Boolean models simulation, and PhysiCell for setting up multicellular agent-based models. This workflow was executed entirely through natural language interactions, without manual coding, direct parameter editing, or manual modification of generated model files. Through this use case, we identified key principles for biological AI-tool integration, specifically regarding tool granularity, session management, and flexible orchestration. Testing across multiple LLMs demonstrated our framework’s portability, though model-dependent variations emphasize the need for rigorous validation. Ultimately, this work establishes a foundation for AI-assisted rapid prototyping, enabling researchers to explore computational hypotheses more rapidly through natural language interaction.</p>

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Intelligent tool orchestration for rapid mechanistic model prototyping: MCP servers as AI-biology interfaces

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Marco Ruscone,
  • Miguel Vazquez,
  • Alfonso Valencia

摘要

Constructing multicellular mechanistic models traditionally requires extensive time and computational expertise. We introduce intelligent tool orchestration via Model Context Protocol (MCP) servers, enabling Large Language Model (LLM) agents to act as AI laboratory assistants for rapid model prototyping. We demonstrate this approach by constructing a multiscale model of cancer cell fate in response to TNF using an AI agent connected to MCP servers interfacing with three complementary tools: NeKo for gene regulatory networks construction, MaBoSS for Boolean models simulation, and PhysiCell for setting up multicellular agent-based models. This workflow was executed entirely through natural language interactions, without manual coding, direct parameter editing, or manual modification of generated model files. Through this use case, we identified key principles for biological AI-tool integration, specifically regarding tool granularity, session management, and flexible orchestration. Testing across multiple LLMs demonstrated our framework’s portability, though model-dependent variations emphasize the need for rigorous validation. Ultimately, this work establishes a foundation for AI-assisted rapid prototyping, enabling researchers to explore computational hypotheses more rapidly through natural language interaction.