CRISPR-GPT for agentic automation of gene-editing experiments.

Journal: Nature biomedical engineering
Published Date: Jul 30, 2025

Abstract

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, large language models (LLMs) often lack domain-specific knowledge and struggle to accurately solve biological design problems. We present CRISPR-GPT, an LLM agent system to automate and enhance CRISPR-based gene-editing design and data analysis. CRISPR-GPT leverages the reasoning capabilities of LLMs for complex task decomposition, decision-making and interactive human-artificial intelligence (AI) collaboration. This system incorporates domain expertise, retrieval techniques, external tools and a specialized LLM fine tuned with open-forum discussions among scientists. CRISPR-GPT assists users in selecting CRISPR systems, experiment planning, designing guide RNAs, choosing delivery methods, drafting protocols, designing assays and analysing data. We showcase the potential of CRISPR-GPT by knocking out four genes with CRISPR-Cas12a in a human lung adenocarcinoma cell line and epigenetically activating two genes using CRISPR-dCas9 in a human melanoma cell line. CRISPR-GPT enables fully AI-guided gene-editing experiment design and analysis across different modalities, validating its effectiveness as an AI co-pilot in genome engineering.

Authors

  • Yuanhao Qu
    Department of Pathology, Department of Genetics, Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA.
  • Kaixuan Huang
    Center for Statistics and Machine Learning, Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA.
  • Ming Yin
    The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China.
  • Kanghong Zhan
    Department of Computing, Data Science, and Society, University of California, Berkeley, Berkeley, CA, USA.
  • Dyllan Liu
    Department of Computer Science, University of California, Berkeley, Berkeley, CA, USA.
  • Di Yin
    Department of Pathology, Department of Genetics, Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA.
  • Henry C Cousins
    Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
  • William A Johnson
    Department of Biology, California Polytechnic State University, San Luis Obispo, CA, USA.
  • Xiaotong Wang
    Clinical Medical College of Acupuncture, Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine.
  • Mihir Shah
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
  • Russ B Altman
    Departments of Medicine, Genetics and Bioengineering, Stanford University, Stanford, California, United States of America.
  • Denny Zhou
    Google DeepMind, Mountain View, CA, USA.
  • Mengdi Wang
    Center for Statistics and Machine Learning, Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, USA. mengdiw@princeton.edu.
  • Le Cong
    Department of Pathology, Department of Genetics, Cancer Biology Program, Stanford University School of Medicine, Stanford, CA, USA. congle@stanford.edu.

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