Author

Manda Sun

Tsinghua Shenzhen International Graduate School

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Synthetic Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:gene regulatory network inference· 1 Approach:large language model· 1 Assay:metabolomics· 1 Organism:multiple species· 1 Organism:yeast· 1

Frequent Co-Authors

Feiran LiiD· 2 shared Jens NielseniD· 2 shared Ke WuiD· 2 shared Yu CheniD· 2 shared Eduard Johannes KerkhoveniD· 1 shared Haohao LiuiD· 1 shared Hongting TangiD· 1 shared Jiahao LuoiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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2 shared papersSystems Biology

Feiran LiORCID

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2 papers

Recent·Most cited

Leveraging large language models for metabolic engineering design

Xiongwen Li, Zhetao Guo, Yi Li, Zhu Liang, Ziyi Liu, Ke Wu, Jiahao Luo, Yuesheng Zhang, Lizheng Liu, Manda Sun, Yuanyuan Huang, Yu Chen, Tao Yu, Jens Nielsen, Hongting Tang, Feiran Li

This paper develops a large language model-based method to extract metabolic engineering strategies from research articles and trains a hybrid model to predict engineering targets, outperforming traditional approaches.

Trends in biotechnology · Apr 23, 2026Peer-reviewedSystems Biology

Yeast-MetaTwin for Systematically Exploring Yeast Metabolism through Retrobiosynthesis and Deep Learning

Ke Wu, Haohao Liu, Manda Sun, Runze Mao, Yindi Jiang, Eduard Johannes Kerkhoven, Yu Chen, Jens Nielsen, Feiran Li

The paper develops Yeast-MetaTwin, a metabolic model for yeast that expands coverage of the metabolome using retrobiosynthesis and deep learning, revealing new insights into underground metabolism.

bioRxiv · Sep 2, 2024 · PreprintSynthetic Biology