| A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism H Lu, F Li, BJ Sánchez, Z Zhu, G Li, I Domenzain, S Marcišauskas, ... Nature communications 10 (1), 3586, 2019 | 268 | 2019 |
| Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction F Li, L Yuan, H Lu, G Li, Y Chen, MKM Engqvist, EJ Kerkhoven, J Nielsen Nature Catalysis 5 (8), 662-672, 2022 | 136 | 2022 |
| Construction of a linker library with widely controllable flexibility for fusion protein design G Li, Z Huang, C Zhang, BJ Dong, RH Guo, HW Yue, LT Yan, XH Xing Applied microbiology and biotechnology 100, 215-225, 2016 | 121 | 2016 |
| Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima G Li, KS Rabe, J Nielsen, MKM Engqvist ACS synthetic biology 8 (6), 1411-1420, 2019 | 118 | 2019 |
| MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in … Y Zhou, G Li, J Dong, X Xing, J Dai, C Zhang Metabolic engineering 47, 294-302, 2018 | 96 | 2018 |
| De novo biosynthesis of bioactive isoflavonoids by engineered yeast cell factories Q Liu, Y Liu, G Li, O Savolainen, Y Chen, J Nielsen Nature communications 12 (1), 6085, 2021 | 79 | 2021 |
| Bayesian genome scale modelling identifies thermal determinants of yeast metabolism G Li, Y Hu, J Zrimec, H Luo, H Wang, A Zelezniak, B Ji, J Nielsen Nature communications 12 (1), 190, 2021 | 45 | 2021 |
| A study on the effects of linker flexibility on acid phosphatase PhoC-GFP fusion protein using a novel linker library Z Huang, G Li, C Zhang, XH Xing Enzyme and Microbial Technology 83, 1-6, 2016 | 37 | 2016 |
| Enhanced Production of Crude Violacein from Glucose in Escherichia coli by Overexpression of Rate-Limiting Key Enzyme(S) Involved in Violacein Biosynthesis Y Zhou, MY Fang, G Li, C Zhang, XH Xing Applied biochemistry and biotechnology 186, 909-916, 2018 | 29 | 2018 |
| Metabolic network remodelling enhances yeast’s fitness on xylose using aerobic glycolysis X Li, Y Wang, G Li, Q Liu, R Pereira, Y Chen, J Nielsen Nature Catalysis 4 (9), 783-796, 2021 | 24 | 2021 |
| Performance of regression models as a function of experiment noise G Li, J Zrimec, B Ji, J Geng, J Larsbrink, A Zelezniak, J Nielsen, ... Bioinformatics and Biology Insights 15, 11779322211020315, 2021 | 20 | 2021 |
| CODY enables quantitatively spatiotemporal predictions on in vivo gut microbial variability induced by diet intervention J Geng, B Ji, G Li, F López-Isunza, J Nielsen Proceedings of the National Academy of Sciences 118 (13), e2019336118, 2021 | 19 | 2021 |
| Basin-scale biogeography of marine phytoplankton reflects cellular-scale optimization of metabolism and physiology JR Casey, RM Boiteau, MKM Engqvist, ZV Finkel, G Li, J Liefer, CL Müller, ... Science Advances 8 (3), eabl4930, 2022 | 18 | 2022 |
| Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection H Lu, F Li, L Yuan, I Domenzain, R Yu, H Wang, G Li, Y Chen, B Ji, ... Molecular Systems Biology 17 (10), e10427, 2021 | 18 | 2021 |
| The pan-genome of Saccharomyces cerevisiae G Li, B Ji, J Nielsen FEMS yeast research 19 (7), foz064, 2019 | 17 | 2019 |
| Genome-scale metabolic modeling from yeast to human cell models of complex diseases: latest advances and challenges Y Chen, G Li, J Nielsen Yeast Systems Biology: Methods and Protocols, 329-345, 2019 | 16 | 2019 |
| Learning deep representations of enzyme thermal adaptation G Li, F Buric, J Zrimec, S Viknander, J Nielsen, A Zelezniak, ... Protein Science 31 (12), e4480, 2022 | 14 | 2022 |
| A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun. 2019; 10: 3586 H Lu, F Li, BJ Sánchez, Z Zhu, G Li, I Domenzain, S Marcišauskas, ... | 13 | 2019 |
| Probabilistic machine learning ensures accurate ambient denoising in droplet-based single-cell omics C Sheng, R Lopes, G Li, S Schuierer, A Waldt, R Cuttat, S Dimitrieva, ... BioRxiv, 2022.01. 14.476312, 2022 | 10 | 2022 |
| Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth Biol 8: 1411–1420 G Li, KS Rabe, J Nielsen, MKM Engqvist | 7 | 2019 |
Jens NielsenProfessor in Systems Biology, Chalmers University of Technology, SwedenVerified email at chalmers.se
