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Related Concept Videos

Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
Entropy and the Second Law of Thermodynamics01:26

Entropy and the Second Law of Thermodynamics

Consider an isolated system in which a hot object is placed in contact with a cold one. This is an irreversible process that eventually leads both objects to reach the same equilibrium temperature. It is crucial to note that the constituents of any substance exhibit increased disorder at higher temperatures. As a cold substance absorbs heat, its constituents become more disordered. The energy transfer from a hotter object to a cooler one increases the system's disorder or randomness. This...
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Second Law of Thermodynamics

In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
Second Law of Thermodynamics00:53

Second Law of Thermodynamics

The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the chemical energy...
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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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.
Path Between Thermodynamics States01:21

Path Between Thermodynamics States

Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:

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Related Experiment Video

Updated: Jun 9, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Generalizing the Gaussian Network Model: Spanning-Tree Thermodynamics Shows Entropy-Driven KRAS Activation.

Fatma Senguler Ciftci1, Burak Erman1

  • 1Department of Chemical and Biological Engineering, Koç University, Istanbul, Turkey.

Proteins
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study reveals how KRAS protein switches between active and inactive states. It uses a new statistical model to show that the active state gains flexibility through entropy, driven by network changes in Switch I.

Keywords:
KRASKirchhoff Laplacianallosteric networkmatrix‐tree theoremspanning‐tree partition function

Related Experiment Videos

Last Updated: Jun 9, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Biophysics
  • Computational Biology
  • Structural Biology

Background:

  • The GTPase KRAS protein switches between active (GTP-bound) and inactive (GDP-bound) states, a critical process in oncogenic signaling.
  • Traditional binary models incompletely characterize the residue-contact organization underlying this conformational switch.

Purpose of the Study:

  • To develop a generalized statistical-mechanical model for analyzing KRAS conformational switching.
  • To investigate the thermodynamic and topological basis of KRAS allostery and its functional versatility.

Main Methods:

  • Generalized the Gaussian Network Model (GNM) using spanning-tree partition functions and the weighted Kirchhoff Laplacian.
  • Employed the Matrix-Tree Theorem for network analysis.
  • Computed thermodynamic properties (free energy, mean contact energy, heat capacity, entropy) across an effective temperature sweep.

Main Results:

  • The generalized GNM framework enables continuous Boltzmann-weighted ensemble analysis.
  • KRAS activation involves an entropy-enthalpy compensation mechanism, with the active state having higher conformational entropy.
  • Switch I (residues 25-40) was identified as the key allosteric site for nucleotide-driven network reorganization.

Conclusions:

  • The study provides a thermodynamically grounded perspective on KRAS allostery.
  • Network architecture enables KRAS functional versatility through entropy-driven conformational flexibility.
  • The developed model offers a powerful tool for probing topological landscapes in protein dynamics.