Sizhe Qiu

This paper introduces MMTKPred, a machine learning model that predicts transporter kinetic parameters Vmax and Km, showing effectiveness across biosystem scales.

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.

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.

This study uses machine learning to predict E. coli transcriptional regulatory networks from DNA sequences, finding that motif strength and DNA shape are key predictors.

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.