  The introduction of genome engineering technology has transformed biomedical
research, making it possible to make precise changes to genetic information.
However, creating an efficient gene-editing system requires a deep
understanding of CRISPR technology, and the complex experimental systems under
investigation. While Large Language Models (LLMs) have shown promise in various
tasks, they often lack specific knowledge and struggle to accurately solve
biological design problems. In this work, we introduce CRISPR-GPT, an LLM agent
augmented with domain knowledge and external tools to automate and enhance the
design process of CRISPR-based gene-editing experiments. CRISPR-GPT leverages
the reasoning ability of LLMs to facilitate the process of selecting CRISPR
systems, designing guide RNAs, recommending cellular delivery methods, drafting
protocols, and designing validation experiments to confirm editing outcomes. We
showcase the potential of CRISPR-GPT for assisting non-expert researchers with
gene-editing experiments from scratch and validate the agent's effectiveness in
a real-world use case. Furthermore, we explore the ethical and regulatory
considerations associated with automated gene-editing design, highlighting the
need for responsible and transparent use of these tools. Our work aims to
bridge the gap between beginner biological researchers and CRISPR genome
engineering techniques, and demonstrate the potential of LLM agents in
facilitating complex biological discovery tasks.
