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CELLM: Bridging Natural Language Processing and Synthetic Genetic Circuit Design with AI.

Lucas Abello Castillo1, Martín Gutiérrez Pescarmona1

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Summary
This summary is machine-generated.

This study introduces CELLM, a novel system integrating large language models with synthetic biology tools like Cello. It enables genetic circuit design via natural language, making synthetic biology more accessible.

Keywords:
CelloSBOLautomationgenetic circuitslarge language modelssynthetic biology

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Area of Science:

  • Synthetic Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Genetic circuit design is complex, hindering synthetic biology's accessibility and efficiency.
  • Current design tools require specialized bioengineering expertise.

Purpose of the Study:

  • To develop an integrated system (CELLM) for creating, analyzing, and optimizing genetic circuits using natural language instructions.
  • To bridge the gap between natural language processing and functional biological design.

Main Methods:

  • Integration of Cello software (v2.1) with large language models (DeepSeek-R1, Phi-4) via the LangChain framework in Python.
  • Automated translation of textual descriptions into genetic circuit designs.
  • Utilizing LLMs for interpreting biological requirements and performing logical optimization.

Main Results:

  • Demonstrated the first system integrating LLMs with synthetic biology design tools like Cello.
  • Successfully translated natural language prompts into functional genetic circuit designs.
  • CELLM automates circuit synthesis and optimization processes.

Conclusions:

  • CELLM establishes a precedent for using LLMs in synthetic biology design.
  • The system significantly lowers the barrier to entry for genetic circuit prototyping.
  • This approach enhances the efficiency and accessibility of synthetic biology research.