Li Feiran

Dr. Feiran Li is an Assistant Professor and Ph.D. supervisor at Tsinghua Shenzhen International Graduate School, Tsinghua University. Her research focuses on developing novel methods for analyzing biological big data and investigating biological systems, aims to construct digital twin life centered on metabolic modeling, synthetic biology and biopharmaceutical research. Her research direction spans computational biology, systems biology, machine learning, chemistry, and drug metabolism.  In recent years, she has published multiple peer-reviewed SCI papers, including first/corresponding-author articles in prestigious journals such as Nature Catalysis, Nature Communications, Molecular Systems Biology, PNAS, and Nucleic Acids Research. She also serves as a Young Editor for Advanced Biotechnology and BioDesign Research and is a reviewer for leading journals including Nature Communications, PNAS, and Genome Biology.

We are currently looking for postdoctoral fellows, research assistants, PhD students, and master's students with backgrounds in synthetic biology, computational biology, machine learning, chemistry, biochemical engineering, bioinformatics, and pharmaceutical engineering with interests in conducting research related to biological system modeling.

2017-2021, Ph.D. in Systems Biology, Chalmers University, Sweden (Supervisor: Prof. Jens Nielsen)

2014-2017, M.S. in Biochemical Engineering, Tianjin University, China (Supervisor: Prof. Zhao Xueming)

2010-2014, B.S. in Chemical Biology, Tianjin Normal University, China

2023-present, Assistant Professor, Tsinghua Shenzhen International Graduate School, China

2021-2023, PostDoc, Chalmers University of Technology, Sweden (Advisor: Prof. Jens Nielsen)

Advanced Biotechnology, BioDeign Research, Youth Editor

Frontiers in Bioengineering and Biotechnology, Review Editor

Nature Communications, PNAS, iScience, Advanced Genetics, and Genome Biology, Reviewer

Artificial Intelligence aided Enzyme Design (SIGS), Frontiers in Synthetic Biology (SIGS)，Computational Systems Biology Experiments (SIGS)

Our group is dedicated to tackling fundamental challenges in modeling biological systems by integrating computational approaches (with a strong emphasis) and experimental techniques. This combined strategy enables deeper insights into biological mechanisms and facilitates molecular discoveries. Our research is situated at the intersection of systems biology, data science, machine learning, and metabolic modeling.

Our long-term objectives are to:

1. Advance metabolic and regulatory models of mammalian cells, organs, and whole-body systems for pharmaceutical and health-related applications (Digital Twin Human);

2. Deepen understanding of the dark matter in cellular metabolism to enable rational design of cell factories (Digital Cell);

3. Develop deep learning models to elucidate relationships between protein sequences, structures, functions, and parameters.

[1] National Science Fund for Excellent Young Scholars (Overseas), 2023/09 – 2026/08, ongoing, PI；

[2] National Key R&D Program Project, 2024/12 – 2027/11, ongoing, co-PI； 

[3] General Program of the National Natural Science Foundation of China, 2025/01-2028/12，ongoing, PI；

[4] Key-Area Research and Development Program of Guangdong Province, ongoing, co-PI； 

[5] Shenzhen Medical Research Fund，2025/1 – 2027/12, ongoing, PI；

[6] Tsinghua University Shenzhen International Graduate School Interdisciplinary Research Fund, 2024/09 – 2027/12，ongoing, PI； 

[7] AI-aided enzyme design-technical development project, 2024/07 – 2026/06, ongoing, PI；

1. Chen Y^*, Li F. Metabolomes evolve faster than metabolic network structures. Proceedings of the National Academy of Sciences 2024, 121, e2400519121.

2. Li F^{#, *}, Chen Y, Gustafsson J, Wang H, Wang Y, Zhang C, Xing X. Genome-scale metabolic models applied for human health and biopharmaceutical engineering. Quantitative Biology. 2023, 11, 363-75.

3. Li F^{#, *}, Chen Y^#, Anton M^#, et al. GotEnzymes: an extensive database of enzyme parameter predictions. Nucleic Acids Research 2023, D1, D583-D586.

4. Li F^#, Yuan L^#, Lu H, et al. Deep learning based kcat prediction enables improved enzyme constrained model reconstruction. Nature Catalysis 2022, 5, 662-672.

5. Li F, Chen Y, Qi Q, et al. Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints. Nature Communications 2022, 13, 2969.

6. Li F^*. Filling gaps in metabolism using hypothetical reactions. Proceedings of the National Academy of Sciences 2022, 119, e2217400119.

7. Lu H^#, Li F^#, Yuan L^#, et al. Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection. Molecular Systems Biology 2021, 17, e10427.

8. Domenzain I^#, Li F^#, Kerkhoven EJ, et al. Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species. FEMS Yeast Research 2021, 21, foab002

9. Lu H^#, Li F^#, Sánchez BJ, et al. A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications 2019, 10, 3586

10. Li F^#, Xie W^#, Yuan Q, Luo H, Li P, et al. Genome-scale metabolic model analysis indicates low energy production efficiency in marine ammonia-oxidizing archaea. AMB Express 2018, 8, 106.

# Co-first author, * Corresponding author

1. MIT Technology Review 35 Innovators Under 35 China (2023)  

2. National Overseas High-Level Talents (Youth) Project (2023)  

3. Pengcheng Peacock Plan Specially Recruited Positions Category B Talent, (2023)  

4. Chinese Government Award for Outstanding Self-Financed Students Abroad – Postdoctoral Researcher (2022)