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Related Concept Videos

Calculating the Equilibrium Constant02:46

Calculating the Equilibrium Constant

38.0K
The equilibrium constant for a reaction is calculated from the equilibrium concentrations (or pressures) of its reactants and products. If these concentrations are known, the calculation simply involves their substitution into the Kc expression.
For example, gaseous nitrogen dioxide forms dinitrogen tetroxide according to this equation:
38.0K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

15.0K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
15.0K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

10.0K
10.0K
The Equilibrium Constant03:10

The Equilibrium Constant

56.7K
Consider the oxidation of sulfur dioxide:
56.7K
Chemical Equilibria: Redefining Equilibrium Constant01:20

Chemical Equilibria: Redefining Equilibrium Constant

1.2K
The effect of an inert salt on the solubility of a sparingly soluble salt is known as the salt effect. The degree of the salt effect varies with the ionic strength of the solution, which in turn depends on the activity of the species in the solution. The activity is expressed as the product of concentration and the activity coefficient of the species.
To calculate the equilibrium constants of solutions of moderately high ionic strength, one must account for the salt effect. This redefined...
1.2K
Free Energy and Equilibrium02:56

Free Energy and Equilibrium

27.2K
The free energy change for a process may be viewed as a measure of its driving force. A negative value for ΔG represents a driving force for the process in the forward direction, while a positive value represents a driving force for the process in the reverse direction. When ΔGrxn is zero, the forward and reverse driving forces are equal, and the process occurs in both directions at the same rate (the system is at equilibrium).
Recall that Q is the numerical value of the mass action...
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Updated: Feb 2, 2026

Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation
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Electrochemical Impedance Spectroscopy as a Tool for Electrochemical Rate Constant Estimation

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Estimating Metabolic Equilibrium Constants: Progress and Future Challenges.

Bin Du1, Daniel C Zielinski1, Bernhard O Palsson2

  • 1Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.

Trends in Biochemical Sciences
|November 27, 2018
PubMed
Summary
This summary is machine-generated.

Estimating reaction equilibrium constants (Keqs) is vital for metabolic networks. The group contribution method faces data and physical property challenges, but new approaches promise improved accuracy for these thermodynamic constraints.

Keywords:
equilibrium constantsgroup contributionmetabolismthermodynamics

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Area of Science:

  • Biochemistry
  • Systems Biology
  • Thermodynamics

Background:

  • Reaction equilibrium constants (Keqs) are crucial for understanding metabolic network function and thermodynamic constraints.
  • The group contribution method is a common approach for estimating Keqs using compound formation energies.

Purpose of the Study:

  • To identify current challenges in the group contribution method for Keq estimation.
  • To explore promising alternative approaches for overcoming these limitations.

Main Methods:

  • Review and analysis of existing literature on the group contribution method.
  • Identification of data quality and physical property representation issues.
  • Discussion of emerging methodologies for Keq prediction.

Main Results:

  • The group contribution method suffers from incomplete/poor quality data and inadequacies in representing compound physical properties.
  • Several promising alternative approaches are emerging to address these limitations.

Conclusions:

  • Addressing the identified challenges in Keq estimation is essential for accurate metabolic network modeling.
  • Advancements in prediction methods will improve the representation of cellular functions under biophysical constraints.