Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Third Law of Thermodynamics02:38

Third Law of Thermodynamics

22.0K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
22.0K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

27.0K
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...
27.0K
Second Law of Thermodynamics00:53

Second Law of Thermodynamics

68.4K
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...
68.4K
DNA Packaging00:58

DNA Packaging

112.5K
Overview
112.5K
First Law of Thermodynamics00:37

First Law of Thermodynamics

80.7K
The First Law of Thermodynamics states that energy cannot be created or destroyed, only transformed. This can be demonstrated within a classic food web where light energy from the sun is harnessed as radiant energy by plants, converted into chemical energy, and stored as complex carbohydrates. The vegetation is then consumed by animals and during the digestion process, the sugars release energy as heat. The sugars also produce chemical energy that either gets used up doing work, stored in...
80.7K
First Law of Thermodynamics02:16

First Law of Thermodynamics

41.0K
Energy Conservation
41.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Generative approaches to kinetic parameter inference in metabolic networks via latent space exploration.

Nature communications·2026
Same author

Metabolic thermodynamics: pertinent reference state and energy potentials.

The FEBS journal·2026
Same author

In silico analysis and comparison of the metabolic capabilities of different organisms by reducing metabolic complexity.

Microbiome·2026
Same author

Identification of new interactors of eIF3f by endogenous proximity-dependent biotin labelling in human muscle cells.

Scientific reports·2025
Same author

Multi-omic assessment of mRNA translation dynamics in liver cancer cell lines.

Scientific data·2025
Same author

Kinetic-model-guided engineering of multiple S. cerevisiae strains improves p-coumaric acid production.

Metabolic engineering·2025

Related Experiment Video

Updated: Jan 31, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K

pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis.

Pierre Salvy1, Georgios Fengos1, Meric Ataman1

  • 1Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Bioinformatics (Oxford, England)
|December 19, 2018
PubMed
Summary

We introduce pyTFA and matTFA, novel tools for Thermodynamics-based Flux Analysis (TFA). These implementations integrate metabolite concentrations and Gibbs energies, enhancing metabolic modeling with crucial thermodynamic constraints.

More Related Videos

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

5.4K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.4K

Related Experiment Videos

Last Updated: Jan 31, 2026

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.9K
A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates
08:41

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

Published on: July 17, 2020

5.4K
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
09:05

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

Published on: June 24, 2019

8.4K

Area of Science:

  • Systems Biology
  • Biotechnology
  • Metabolic Engineering

Background:

  • Omics data analysis is crucial in Systems Biology and Biotechnology.
  • Traditional Flux Balance Analysis (FBA) often neglects thermodynamic constraints.
  • Thermodynamics-based Flux Analysis (TFA) integrates quantitative metabolomics data and thermodynamic equilibrium information.

Purpose of the Study:

  • To present the first published implementations of the TFA framework.
  • To enable straightforward integration of metabolite concentration measurements into metabolic models.
  • To demonstrate the utility of TFA in reducing the feasible flux space compared to FBA.

Main Methods:

  • Development of a Python package (pyTFA) and a Matlab toolbox (matTFA).
  • Explicit formulation of Gibbs energies and metabolite concentrations within the TFA framework.
  • Application of pyTFA and matTFA on reduced and genome-scale models of E. coli.

Main Results:

  • pyTFA and matTFA facilitate the integration of metabolite concentration data.
  • TFA implementation allows estimation of reaction thermodynamic equilibrium.
  • TFA reduces the feasible flux space compared to standard FBA, providing critical insights.

Conclusions:

  • pyTFA and matTFA are valuable tools for implementing TFA.
  • These tools enhance metabolic modeling by incorporating thermodynamic constraints.
  • The implementations are publicly available for the scientific community.