FEIRAN LI

 

FEIRAN LI

Assistant Professor, PhD supervisor

Tsinghua University, Tsinghua Shenzhen International Graduate School, Institute of biopharmaceutical and health engineering

 

 

 

Education and work experience

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

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

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

 

Research Interest

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 (AI Virtual Cell);
  3. Develop deep learning models to elucidate relationships between protein sequences, structures, functions, and parameters.

 

Awards

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)

 

Selected Publications

  1. 1.Chen Y*, Li F. Metabolomes evolve faster than metabolic network structures. Proceedings of the National Academy of Sciences 2024, 121, e2400519121.
  2. 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. 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. 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. 5.LiF, ChenY, QiQ, etal. Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints. Nature Communications2022, 13, 2969.
  6. 6.Li F*. Filling gaps in metabolism using hypothetical reactions. Proceedings of the National Academy of Sciences 2022, 119, e2217400119.
  7. 7.Lu H#,Li F#,Yuan L#,et al.Yeast metabolicinnovations emergedviaexpanded metabolic network and gene positive selection. Molecular Systems Biology 2021,17, e10427.
  8. 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. 9.Lu H#, Li F#, Sánchez BJ, et al. Aconsensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications 2019, 10, 3586
  10. 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

Others:

Google scholar：https://scholar.google.com/citations?user=Zn6Gy-IAAAAJ&hl=en

ORCID: https://orcid.org/0000-0001-9155-5260

 

Contact Information

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Tsinghua Shenzhen International Graduate School, Phase I, 16F, A1603