Feiran Li

A benchmarking framework for single-cell genome-scale metabolic model construction

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

Function-guided design of active enzymes

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

Leveraging large language models for metabolic engineering design

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.

MicroGenomer: A Foundation Model for Transferable Microbial Genome Representations Enabling Multi-scale Genomic Understanding and Ecophysiological Trait Prediction

The Role of Metabolism in Shaping Enzyme Structures Over 400 Million Years of Evolution

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.

FROG Analysis Ensures the Reproducibility of Genome Scale Metabolic Models

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

Yeast-MetaTwin for Systematically Exploring Yeast Metabolism through Retrobiosynthesis and Deep Learning

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: A Consensus Yeast Metabolic Model Enables Quantitative Analysis of Cellular Metabolism By Incorporating Big Data

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

Standard-GEM supports reproducible development of genome-scale metabolic models

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

Deep learning based kcat prediction enables improved enzyme constrained model reconstruction

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.

Genome scale modeling of the protein secretory pathway reveals novel targets for improved recombinant protein production in yeast

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: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework

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.