Yuichi Sakumura

The paper shows actin filaments move directionally via treadmilling, driving membrane protrusions and cell migration through physical mechanisms.

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 establishes mechanical polarity as a physical principle linking adhesion dynamics to directional stability in glioblastoma cell migration.

This paper develops a CNN-based method to classify estrous cycle phases in mice using external genital images, achieving over 83% accuracy with a custom lightweight model.

The paper introduces a machine learning model for traction force microscopy that improves accuracy under sparse observation conditions by incorporating cell morphology and biophysical mechanisms.

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.