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Protein Dynamics in Living Cells01:19

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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Scientists identified the plasma membrane in the 1890s and its principal chemical components (lipids and proteins) by 1915. The model for plasma membrane structure, proposed in 1935 by Hugh Davson and James Danielli, was the first model to be widely accepted in the scientific community. The model was based on the plasma membrane's "railroad track" appearance in early electron micrographs. Davson and Danielli theorized that the plasma membrane's structure resembled a sandwich with the analogy of...
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The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.
Equilibrium Conditions for a Particle01:23

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When an object is in equilibrium, it is either at rest or moving with a constant velocity. There are two types of equilibrium: static and dynamic. Static equilibrium occurs when an object is at rest, while dynamic equilibrium occurs when an object is moving with a constant velocity. In both cases, there must be a balance of forces acting on the object.
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Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.

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Related Experiment Video

Updated: May 18, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

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Fluctuation-preserving coarse graining for biochemical systems.

Bernhard Altaner1, Jürgen Vollmer

  • 1Max-Planck-Institut für Dynamik und Selbstorganisation, 37077 Göttingen, Germany.

Physical Review Letters
|September 26, 2012
PubMed
Summary

We present a systematic method to simplify finite stochastic Markov models used in biology. This approach better preserves connections between different scales and maintains fluctuations compared to other methods.

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Area of Science:

  • Computational Biology
  • Biophysics
  • Systems Biology

Background:

  • Finite stochastic Markov models are crucial for modeling biological systems at a mesoscopic level.
  • These models represent coarse-grained descriptions of underlying microscopic dynamics.
  • The degree of coarse-graining is often determined by measurement resolution.

Purpose of the Study:

  • To introduce a systematic method for simplifying stochastic descriptions of biological systems.
  • To preserve connections between microscopic and mesoscopic scales (meso-micro) and between mesoscopic and macroscopic scales (meso-macro).
  • To improve the preservation of observable fluctuations compared to existing simplification approaches.

Main Methods:

  • The simplification method ensures locality to maintain meso-micro connections.
  • Meso-macro connections are preserved by analyzing cycles within the network of states.
  • The approach focuses on simplifying the stochastic dynamics while retaining key properties.

Main Results:

  • The proposed method offers a systematic way to reduce model complexity.
  • It effectively preserves the relationships between different levels of biological organization.
  • Fluctuations of observables are maintained more accurately than with naive simplification techniques.

Conclusions:

  • This systematic simplification technique enhances the utility of stochastic Markov models in biology.
  • The method provides a robust way to bridge different scales in biological modeling.
  • It offers a significant improvement in preserving dynamic properties of biological systems.