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Maxwell's Thermodynamic Relations01:23

Maxwell's Thermodynamic Relations

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Maxwell's thermodynamic relations are very useful in solving problems in thermodynamics. Each of Maxwell's relations relates a partial differential between quantities that can be hard to measure experimentally to a partial differential between quantities that can be easily measured. These relations are a set of equations derivable from the symmetry of the second derivatives and the thermodynamic potentials.
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Thermal Sigmatropic Reactions: Overview01:16

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Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
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Activity is the measure of the effective concentration of the species in solution. It can be expressed as the product of the molar concentration of the species and its activity coefficient. The activity coefficient is a dimensionless quantity and depends on the total ionic strength of the solution.
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Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...
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Thermodynamic Systems01:06

Thermodynamic Systems

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A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
Consider an example of  tea boiling in a kettle. The...
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Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

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Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
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Updated: Nov 14, 2025

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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multiTFA: a Python package for multi-variate thermodynamics-based flux analysis.

Vishnuvardhan Mahamkali1, Tim McCubbin1, Moritz Emanuel Beber2

  • 1Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD 4072, Australia.

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This study introduces multiTFA, improving thermodynamic-based flux analysis (TFA) by handling multiple thermodynamic variables. The new method significantly reduces uncertainty in thermodynamic variables for metabolic models.

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Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Thermodynamic-based flux analysis (TFA) is crucial for understanding metabolic networks.
  • Accurate thermodynamic variables are essential for reliable TFA.
  • Existing methods often struggle with uncertainty in thermodynamic variable estimation.

Purpose of the Study:

  • To develop an improved framework for thermodynamic-based flux analysis.
  • To reduce the uncertainty associated with thermodynamic variables in metabolic models.
  • To provide a practical implementation for researchers.

Main Methods:

  • Introduced multivariate treatment of thermodynamic variables.
  • Leveraged component contribution, an advanced group contribution methodology.
  • Developed multiTFA, a Python implementation of the framework.

Main Results:

  • Achieved significant reduction in uncertainty of thermodynamic variables.
  • Demonstrated a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges using the *Escherichia coli* core model.
  • Observed three out of 12 glycolysis reactions changing from reversible to irreversible.

Conclusions:

  • The multiTFA framework offers a substantial improvement over existing TFA methods.
  • Reduced uncertainty in thermodynamic variables enhances the predictive power of metabolic models.
  • The open-source implementation facilitates wider adoption and further research in metabolic network analysis.