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Protein Dynamics in Living Cells

Protein Dynamics in Living Cells

Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...

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Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

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Reconstructing dynamic molecular states from single-cell time series.

Lirong Huang¹, Loic Pauleve², Christoph Zechner³

¹Institute of Molecular Systems Biology, D-BIOL, ETH Zurich, Zurich, Switzerland.

Journal of the Royal Society, Interface

|September 9, 2016

Summary

Researchers developed a computational method to reconstruct the full system state of stochastic biomolecular networks from partial experimental data. This approach enables a deeper understanding of complex intracellular processes and gene expression dynamics.

Keywords:

chemical master equation continuous time Markov chains gene expression moment dynamics optimal filtering

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Area of Science:

Systems biology
Computational biology
Biophysics

Background:

Understanding complex biological systems requires defining their state, which is challenging due to intracellular process complexity.
Experimental methods often yield only partial information about system states.

Purpose of the Study:

To develop a mathematical and computational framework for reconstructing full system states from partial experimental data.
To apply this framework to stochastic biomolecular reaction networks, specifically gene expression systems.

Main Methods:

Utilized mathematical analysis and computational modeling to convert partial state information into a full system state.
Introduced the posterior master equation and derived approximations for posterior moment dynamics.
Applied conditional Markov process concepts.

Main Results:

Successfully reconstructed dynamic promoter and mRNA states from noisy protein abundance measurements in Saccharomyces cerevisiae.
Demonstrated the state reconstruction approach using both in silico and single-cell data.

Conclusions:

The developed method enables comprehensive state reconstruction for stochastic biomolecular systems.
This approach advances the principled understanding of intracellular processes and gene expression regulation.