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An AI-native experimental laboratory for autonomous biomolecular operations

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Data availability

The data supporting the findings of this study are available in the Article and its Supplementary Information. The sequencing data generated in the enzymatic DNA synthesis and DNA data storage experiments, together with the agent-related outputs, are available at Zenodo (https://doi.org/10.5281/zenodo.20624108)53. Reference genome hg38 was downloaded from https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000001405.40/. Further data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

The code is available at GitHub (https://github.com/IPADS-X/AutoDNA). The version used in this study is available at Zenodo (https://doi.org/10.5281/zenodo.20624108)53.

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Acknowledgements

During the preparation of this work, ChatGPT (OpenAI) and DeepSeek were used to assist with language editing. All AI-assisted content was reviewed and edited by the authors. No generative AI tools were used to generate or modify experimental data or scientific conclusions.

Funding

This work was supported by the National Natural Science Foundation of China (T2188102, 62132014 and 62422209), the Shanghai Pilot Program for Basic Research, the Shanghai Municipal Science and Technology Major Project, the Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM113 and JYB2025XDXM602) and the New Cornerstone Science Foundation.

Author information

Author notes

  1. These authors contributed equally: Mingyu Wu, Zhaoguo Wang, Jiabin Wang

Authors and Affiliations

  1. Institute of Parallel and Distributed Systems, School of Computer Science, Institute of Artificial Intelligence Operating Systems, Engineering Research Center for Domain-specific Operating Systems (Ministry of Education), Zhang Jiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, China

    Mingyu Wu, Zhaoguo Wang, Zhiyuan Dong, Jingkai Yang, Tianyu Huang & Haibo Chen

  2. State Key Laboratory of Synergistic Chem-Bio Synthesis, School of Chemistry and Chemical Engineering, New Cornerstone Science Laboratory, Frontiers Science Center for Transformative Molecules, Zhang Jiang Institute for Advanced Study and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China

    Jiabin Wang, Qingting Li, Chi Zhang, Lei Zhao, Mingqiang Li, Xiaolei Zuo, Fei Wang & Chunhai Fan

  3. Jiaxing Key Laboratory of Biosemiconductors (A); Xiangfu Laboratory, Jiashan, China

    Jiabin Wang

  4. Yuanzi Zhishu Technology Co. Ltd, Shanghai, China

    Zhiyuan Dong

  5. School of Biomedical Engineering, Department of Gynecologic Oncology, The International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University, Shanghai, China

    Ping Song

  6. Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acids Chemistry and Nanomedicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Xiaolei Zuo

Authors

  1. Mingyu Wu
  2. Zhaoguo Wang
  3. Jiabin Wang
  4. Zhiyuan Dong
  5. Jingkai Yang
  6. Qingting Li
  7. Tianyu Huang
  8. Chi Zhang
  9. Ping Song
  10. Lei Zhao
  11. Mingqiang Li
  12. Xiaolei Zuo
  13. Fei Wang
  14. Chunhai Fan
  15. Haibo Chen

Contributions

C.F. and H.C. conceived the research. Z.W. led and supervised the research. M.W. and Z.W. designed the multi-agent architecture and constructed the AutoDNA experimental platform. J.W. and Z.D. contributed to most aspects of the experimental design and wet-lab preparation. J.Y., T.H. and Z.D. implemented the agents and LLM workflows. C.Z. conducted the manual DNA synthesis control experiments. P.S. and X.Z. contributed to the experimental design for cancer detection and provided clinical samples. M.W. and Z.W. analysed the data and wrote the paper. J.W., L.Z., M.L., Q.L., F.W., C.F. and H.C. participated in the data analysis and discussions. Z.W., F.W., C.F. and H.C. reviewed and edited the Article.

Corresponding authors

Correspondence to Zhaoguo Wang, Fei Wang, Chunhai Fan or Haibo Chen.

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Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature thanks Milad Abolhasani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 The automated hardware platform of AutoDNA.

a, The platform contains a series of workstations with distinct functionalities. b, A robotic arm (AUBO-i10) is responsible for transferring reagents among workstations.

Extended Data Fig. 2 Engineering design of the AutoDNA system for automated Nanopore sequencing.

a, Automated operation of Nanopore Mk1C is enabled by a high-precision robotic arm guided by a dual-camera machine vision system. b, The original cover of the sequencing flow cell is replaced with a custom-designed one engineered for robotic gripping. c, The robotic arm’s gripper can be directly used for the installation and removal of the custom cover. d, A high-precision camera (GTS-8016) is used to precisely locate the flow cell’s sampling port (~2 mm in diameter). e, An automated pipetting module (Keyto-SP16) dispenses liquid directly into the identified sampling port of the flow cell.

Extended Data Fig. 3 The user interface of AutoDNA.

The main panel displays the output of the current step, corresponding to the step highlighted in the Agent Workflow panel on the right.

Extended Data Fig. 4 Comparison of AutoDNA and manual NAT performance.

a,b, Representative real-time fluorescence curves from one of three independent experiments for the NAT assay executed by the AutoDNA (a) and by manual operation (b).

Source data

Extended Data Fig. 5 Normalized fluorescence kinetics in the strand-displacement assay for siRNA validation.

a, Commercially synthesized siRNA as the positive control and the corresponding no-template control (NTC). b, siRNA generated by AutoDNA and the corresponding NTC. c, An incorrect siRNA sequence as the negative control and the corresponding NTC. Solid lines represent mean values and shaded areas indicate s.d. from three independent experiments (n = 3 per curve).

Source data

Extended Data Fig. 6 PAGE analysis results during iterative optimizations.

Each iteration included four separately synthesized samples. Known-length oligonucleotides are indicated as size references. For gel source data, see Supplementary Fig. 1.

Source data

Extended Data Fig. 7 Timeline comparison of concurrent and sequential execution time.

Four enzymatic DNA synthesis protocols were executed concurrently after static code merging (Concurrent-1–4) or sequentially (Sequence). Coloured segments show the timing and duration of individual instrument operations, and arrows indicate protocol completion. The timeline shows a representative run from two independent experiments.

Source data

Extended Data Fig. 8 Real-time qPCR amplification curves for MUT, WT, and no-template control (NTC) samples using commercially synthesized primers.

a, Representative real-time qPCR amplification curves from one of four independent experiments. The MUT sample shows an earlier and stronger fluorescence increase compared with WT, while NTC remains flat throughout most of the amplification cycles, indicating good specificity and low background signal. b, Corresponding Cq values for MUT, WT and NTC. MUT exhibits the lowest Cq value, followed by WT, whereas NTC shows a substantially delayed signal with a Cq value close to the end of the tested cycle range. Data are presented as mean ± s.d. from four independent experiments (n = 4 per sample), with three technical replicates per sample in each independent experiment. Each data point represents the mean of the three technical replicates within one independent experiment.

Source data

Supplementary information

Source data

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Wu, M., Wang, Z., Wang, J. et al. An AI-native experimental laboratory for autonomous biomolecular operations. Nature (2026). https://doi.org/10.1038/s41586-026-11228-6

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