Katsuyuki Kunida

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 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.

This paper demonstrates a hybrid in silico/in-cell controller that reduces toxicity from excessive enzyme overexpression by adapting to process-model mismatch in engineered E. coli.

This paper introduces a hybrid control system for microbial bioprocesses that addresses process-model mismatch by combining in silico and in-cell controllers.

The paper introduces MTA to decode cell edge velocity from Rho GTPase activities using pseudo-simultaneous observations, showing coordinated regulation by Cdc42, Rac1, and RhoA.

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