Yihui Shen

The paper introduces a new genome-scale metabolic model, iHME, which improves prediction of metabolic dependencies in cancer cells with higher accuracy and lower computational cost.

The paper develops a model linking matrix stiffness and ligand density to cellular bioenergetics in cancer cells, showing how mechanical forces influence metabolic pathways and contractility.

The paper explains aerobic glycolysis as a result of mitochondrial respiration saturation and decoupling of mitochondrial biogenesis from other cellular processes.

This paper describes an end-to-end pipeline for cost-effective succinic acid production using Issatchenkia orientalis, achieving high titers and financial viability with reduced greenhouse gas emissions.

The paper shows mitochondrial respiration is more proteome-efficient than aerobic glycolysis in yeast and mammals, with flexibility arising from maintaining a fermentation-capable proteome.

The paper introduces NetID, a global network optimization method for annotating untargeted metabolomics data, identifying novel metabolites in yeast and mouse datasets.

The paper constructs a genome-scale metabolic model for Rhodosporidium toruloides IFO0880, integrating functional genomics and phenotypic data to improve predictions of gene essentiality and metabolic capabilities.