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Protein Diffusion in the Membrane

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...

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Related Experiment Video

Updated: Apr 25, 2026

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

Micro-heterogeneity metrics for diffusion in soft matter.

John Mellnik¹, Paula A Vasquez, Scott A McKinley

¹Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

|August 22, 2014

Summary

This summary is machine-generated.

This study introduces new methods to detect and cluster heterogeneous paths in soft matter, improving characterization of diffusive transport properties in complex biological materials.

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Area of Science:

Soft Matter Physics
Biophysics
Statistical Mechanics

Background:

Passive particle tracking is key for characterizing soft matter transport properties.
Ensemble averaging is standard but insufficient for heterogeneous biological materials.
Understanding heterogeneity is crucial for biologically relevant transport properties.

Purpose of the Study:

To detect heterogeneity in diffusive paths using passive particle tracking data.
To decompose particle paths into statistically distinct clusters.
To apply novel statistical and machine learning methods to soft matter analysis.

Main Methods:

Analysis of passive particle tracking path data.
Application of statistical and machine learning techniques.
Heterogeneity detection and cluster decomposition of particle paths.

Main Results:

Developed methods to detect heterogeneity directly from path data.
Successfully decomposed particle paths into distinct statistical clusters.
Demonstrated effectiveness on diverse simulated and experimental datasets.

Conclusions:

Novel methods enable detection and clustering of heterogeneity in diffusive processes.
These techniques enhance characterization of transport in complex soft matter.
Applicable to various materials including biological fluids and gels.