Feiran Li

This study benchmarks strategies for constructing single-cell genome-scale metabolic models across nine scRNA-seq datasets, evaluating accuracy, sensitivity, and computational feasibility.

The paper presents a framework for designing active enzymes from functional descriptions, achieving high catalytic activity in synthesized designs.

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.

This study uses AlphaFold2 to analyze how metabolism influenced enzyme structural evolution across 400 million years by examining 11,269 enzyme structures and their metabolic reactions.

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

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.

Yeast9 improves yeast metabolic modeling by integrating multi-omics data, showing better prediction of growth rates and gene function than transcriptomics.

Standard-GEM provides a template for reproducible genome-scale metabolic model development following FAIR principles and software best practices.

A substrate graph neural network plus protein-sequence CNN predicts enzyme kcat (test Pearson r 0.71), tracks mutation effects, and parameterizes enzyme-constrained models for 343 yeast/fungi species.

The paper develops a genome-scale model of the yeast protein secretory pathway to identify targets for improving recombinant protein production, finding that the secretory pathway has more limited capacity than metabolism.

SLIMEr improves genome-scale metabolic models by accurately representing lipid requirements using experimental data, enabling analysis of lipid distribution flexibility and energy costs in yeast.