Stochastic resonance with differential code in feedforward network with intra-layer random connections

Decoding cellular deformation from pseudo-simultaneously observed Rho GTPase activities

Limitations in simultaneously observing the activity of multiple molecules in live cells prevent researchers from elucidating how these molecules coordinate the dynamic regulation of cellular functions. Here, we propose the motion-triggered average (MTA) algorithm to characterize pseudo-simultaneous dynamic changes in arbitrary cellular deformation and molecular activities. Using MTA, we successfully extract a pseudo-simultaneous time series from individually observed activities of three Rho GTPases: Cdc42, Rac1, and RhoA. To verify that this time series encoded information on cell-edge movement, we use a mathematical regression model to predict the edge velocity from the activities of the three molecules. The model accurately predicts the unknown edge velocity, providing numerical evidence that these Rho GTPases regulate edge movement. Data preprocessing using MTA combined with mathematical regression provides an effective strategy for reusing numerous individual observations of molecular activities.