Shinya Kuroda

The paper identifies dynamical signatures and a mathematical principle underlying the transition from normothermia to hypothermia in mammals.

The paper maps the Gibbs free energy landscape of liver metabolism in mice during fasting and obesity, revealing thermodynamic robustness despite metabolic changes.

This study creates a transcriptomic atlas of the Syrian hamster preoptic area using snRNA-seq and spatial transcriptomics, comparing seasonal and hibernation states.

The paper integrates multi-omic data from Alzheimer's disease patients to reveal coordinated downregulation of energy-producing metabolic pathways.

MetDeeCINE predicts enzyme-metabolite relationships using multi-omics data and outperforms existing models in predicting concentration control coefficients in E. coli.

The paper examines metabolic adaptations during starvation in obese mice, finding impaired responsiveness and elevated enzyme expression in skeletal muscle and liver.

The paper examines how obesity disrupts the temporally ordered metabolic network in the liver during starvation, revealing structural robustness but temporal vulnerability.

This study uses mathematical modeling to show insulin selectively regulates lipid and BCAA metabolism in humans, with distinct effects on influx and efflux.

The paper compares metabolic dysregulation in obese mice during feeding and fasting using multi-omics data, revealing opposite metabolic shifts linked to obesity.

iTraNet is a web tool for visualizing and analyzing trans-omics networks, revealing differences in hub molecule responses to glucose between wild-type and ob/ob mice.

The paper introduces DI/cle, a new index combining insulin sensitivity, secretion, and clearance, showing it is impaired in T2DM but not in IGT.

This study compares hypothesis-driven and data-driven analyses using tensor decomposition on human blood metabolome data, finding that tensor decomposition can extract similar biological features more efficiently.

This study uses multiomic analysis of skeletal muscle in obese and healthy mice to reveal differences in metabolic regulation by metabolites and genes after glucose administration.

This paper introduces OMELET, a method to infer metabolic flux from multi-omic data, and applies it to identify obesity-associated dysregulation in liver glucose metabolism.

The paper develops a mathematical model to predict an optimal glucose ingestion pattern that minimizes peak blood glucose levels in humans, validated through experimental testing.

The paper analyzes trans-omic data from healthy and obese mice to reveal that glucose-responsive liver metabolism is regulated by rapid changes in metabolites and gene expression in the healthy state, and by slow transcriptional regulation in the obese state.

The paper shows that small intracellular and large intercellular variations enhance information transmission, enabling precise tissue function control.

The paper develops a microfluidic system to control extracellular signal molecule concentrations and measures intracellular Ca2+ responses in CHO-K1 cells.

The paper uses stochastic simulations to show that small, numerous dendritic spines enhance robustness and efficiency of information transfer by reducing noise impact and increasing parallel processing.

The paper develops a mathematical model to show that bidirectional changes in hepatic and peripheral insulin clearance explain abnormal serum insulin concentration patterns in diabetic subjects.

The paper develops a system identification method using compressed sensing to analyze signaling-decoding systems with different time scales, revealing gene expression responses to signaling activation.

The paper constructs a trans-omic network of insulin action using phosphoproteomic, transcriptomic, and metabolomic data to identify fed and fasting insulin signals.

The paper measures cell-to-cell variability in MAPK signaling and shows upstream noise drives ERK phosphorylation heterogeneity, with the system using kinetic proof-reading to robustly transmit signals.