Li Feiran

2021, Ph.D. in Systems Biology, Chalmers University, Sweden, Advisor: Prof. Jens Nielsen

2017, MS in Biochemical engineering, Tianjin University, China

2014, Bachelor of Science, Tianjin Normal University, China

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

2021-2023, PostDoc, Chalmers University of Technology, Sweden

Frontiers in Bioengineering and Biotechnology, Review Editor

PNAS, iScience and Genome Biology, Reviewer

Our group focuses on addressing the fundamental challenges in biological system modeling by employing both computational (strong emphasis) and experimental techniques to enable insights about biological mechanisms and molecular discovery. Our research is situated at the intersection of systems biology, data science, machine learning, and metabolic modeling.

Our long-term objectives are to:

1.    accelerate metabolic or regulatory models of mammalian cells, organs, and whole-body for pharmaceutical and health-related applications,

2.    enhance understanding of dark matter of cellular metabolism for rational cell factory design, and

3.    develop AI-driven models to aid in understanding the relationship among protein sequence, function, and parameters.

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.

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

2.      Li F^#, Yuan L^#, Lu H, Li G, Chen Y, Engqvist MKM, Kerkhoven EJ and Nielsen J. Deep learning based kcat prediction enables improved enzyme constrained model reconstruction. Nature Catalysis 2022, 5, 662-672. 

3.      LiF, ChenY, QiQ, WangY, YuanL, HuangM, ElsemmanIE, FeiziA, etal. Improving recombinant protein production by yeast through genome-scale modeling using proteome constraints. Nature Communications2022, 13, 2969.

4.      Li F^*. Filling gaps in metabolism using hypothetical reactions. Proceedings of the National Academy of Sciences 2022.

5.      Lu H^#,Li F^#,Yuan L^#,Domenzain I,Yu R,et al.Yeast metabolicinnovations emergedviaexpanded metabolic network and gene positive selection. Molecular Systems Biology 2021,17, e10427.

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

7.      Lu H^#, Li F^#, Sánchez BJ, Zhu Z, Li G, et al. Aconsensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nature Communications 2019, 10, 3586

8.      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.

# Co-first author, * Corresponding author

For the full list, please check:

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

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