Aidong Yang

The paper models the efficiency of rhodopsin-augmented CO2-to-biomass conversion in Cupriavidus necator, finding light benefits depend on pathway and reactor conditions.

This study uses AI to design novel microbial rhodopsins that absorb blue light and enhance bacterial growth.

This paper builds a proteome-constrained metabolic model for Sus scrofa muscle stem cells to simulate their growth and metabolism under varying lactate and ammonium conditions, aiding in cultured meat production optimization.

This study introduces CatOpt, a deep learning model that predicts enzyme optimal pH and designs point mutations to improve acid resistance, achieving a 7% activity increase in a specific enzyme.

The paper introduces an information-theoretic framework to identify key tuning residues in proteins by denoising sequence data, validated across multiple datasets with significant performance improvements.

This study quantifies acid stress response in Lactiplantibacillus plantarum and models exopolysaccharide production, revealing a proteome trade-off between primary metabolism and EPS biosynthesis.

Seq2Topt predicts enzyme optimal temperature using protein sequences with deep learning, achieving high accuracy and identifying key residues for temperature adaptation.

DLTKcat predicts temperature-dependent enzyme turnover rates (kcat) using deep learning, achieving better performance than prior models.

The paper uses independent component analysis to identify independently modulated genes in Lactobacillus plantarum and finds a trade-off between secondary and primary metabolism regulation.