Extracting quantitative relationships between cell motility and molecular activities (Analytical approaches and implications)

Abstract

Despite considerable advancements in biological measurement technologies, capturing the simultaneous temporal changes in various biomolecular concentrations remains a challenge. Overcoming this technical difficulty via data preprocessing could not only clarify the principles of biological functions but also reduce the costs associated with advancing measurement technologies. This review introduces a novel approach to harmonizing heterogeneous time-series data related to molecular signaling and cellular movement. In response to this challenge, we developed and employed a motion-trigger average (MTA) algorithm. The MTA comprehensively screens and averages intracellular molecular activities that coincide with targeted velocity patterns of the moving cell edge. Given that the MTA filters out cell individuality-dependent noise from the data, a straightforward regression equation can correlate edge moving velocities with the molecular activities of various species within the cell. This methodology not only integrates fragmented datasets but also enables the reuse of past data for new analyses. The crux of our discovery is the elucidation of the role that Rho GTPases play in regulating cellular edge dynamics, a finding made possible by adopting the MTA algorithm. Our study suggests that the MTA could become an indispensable tool in data-driven biology, potentially paving the way for considerable insights into dynamic cellular behaviors and the underlying biological principles.

Keywords

• motion-triggered average
• time-series data
• molecular signaling
• cell motility
• quantitative regression model