CRISPR-GPT for Agentic Automation of Gene-editing Experiments
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908496489873408 |
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| author | Qu, Yuanhao Huang, Kaixuan Yin, Ming Zhan, Kanghong Liu, Dyllan Yin, Di Cousins, Henry C. Johnson, William A. Wang, Xiaotong Shah, Mihir Altman, Russ B. Zhou, Denny Wang, Mengdi Cong, Le |
| author_facet | Qu, Yuanhao Huang, Kaixuan Yin, Ming Zhan, Kanghong Liu, Dyllan Yin, Di Cousins, Henry C. Johnson, William A. Wang, Xiaotong Shah, Mihir Altman, Russ B. Zhou, Denny Wang, Mengdi Cong, Le |
| contents | 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. The published version of this draft is available at https://www.nature.com/articles/s41551-025-01463-z. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18021 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CRISPR-GPT for Agentic Automation of Gene-editing Experiments Qu, Yuanhao Huang, Kaixuan Yin, Ming Zhan, Kanghong Liu, Dyllan Yin, Di Cousins, Henry C. Johnson, William A. Wang, Xiaotong Shah, Mihir Altman, Russ B. Zhou, Denny Wang, Mengdi Cong, Le Artificial Intelligence Computation and Language Human-Computer Interaction Quantitative Methods 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. The published version of this draft is available at https://www.nature.com/articles/s41551-025-01463-z. |
| title | CRISPR-GPT for Agentic Automation of Gene-editing Experiments |
| topic | Artificial Intelligence Computation and Language Human-Computer Interaction Quantitative Methods |
| url | https://arxiv.org/abs/2404.18021 |