Toshiya Kokaji

This paper introduces a method to expand gene regulatory networks by incorporating prior knowledge, outperforming conventional methods in distinguishing true interactions from indirect associations.

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

The paper uses a stepwise Bayesian framework to identify a novel gene regulatory network component in neural tube development using whole-genome sequencing data from neural precursor cells treated with Sonic Hedgehog.

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

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

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