A computational approach for detecting micro-domains and confinement domains in cells: a simulation study

Area of Science:

• Cellular Biology
• Biophysics
• Computational Biology

Background:

• Cellular processes involve molecular confinement within specific regions, known as trapping areas or microdomains.
• Understanding these areas is crucial for comprehending molecular dynamics and subdiffusion.
• Current methods for identifying these regions can be limited.

Purpose of the Study:

• To develop and validate an original computational approach for detecting cellular trapping areas.
• To automatically identify regions characterized by a high concentration of subdiffusive particles.
• To distinguish between subdiffusion, superdiffusion, and Brownian motion in molecular trajectories.

Main Methods:

• Utilizing a computational approach that processes molecule trajectories.
• Employing a combination of clustering algorithms and trajectory classification.
• Distinguishing different types of molecular motion, including subdiffusion.

Main Results:

• Successfully identified trapping areas by analyzing molecular trajectories.
• Demonstrated the method's efficacy on simulated data generated by Fluosim.
• Illustrated the potential of the approach using real Total Internal Reflection Fluorescence (TIRF) microscopy data.

Conclusions:

• The proposed computational method effectively detects cellular trapping areas.
• This approach offers a novel way to analyze molecular confinement and subdiffusion.
• The findings have implications for studying cellular dynamics and molecular interactions.