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

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Thermodynamic Systems01:06

Thermodynamic Systems

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 tea and...
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:
Thermodynamic Potentials01:26

Thermodynamic Potentials

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...
Thermodynamic Processes01:25

Thermodynamic Processes

A thermodynamic process is a path through a sequence of states that takes a system from an initial state to a final state. In a cyclic process, the system returns to its initial state, so the changes in state properties and state functions (ΔT, Δp, ΔV, ΔU, ΔH) over one complete cycle are zero. However, heat and work transfers can still occur during the cycle, and the net heat and net work over the cycle need not be zero.A reversible process occurs when the system is infinitesimally close to...
Isothermal Processes01:21

Isothermal Processes

A thermodynamic process that occurs at constant temperature is called an isothermal process. Heat slowly flows into the system or out of the system to maintain thermal equilibrium. Processes involving phase changes like water evaporation into steam or freezing water into ice at a constant temperature are examples of Isothermal Processes.
An ideal gas can also undergo isothermal expansion or compression.
For example, consider 1 mole of an ideal gas inside an isolated cylinder at initial volume V...

You might also read

Related Articles

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

Sort by
Same author

On the geometry of elementary flux modes.

Journal of mathematical biology·2023
Same author

MERRIN: MEtabolic regulation rule INference from time series data.

Bioinformatics (Oxford, England)·2022
Same author

Time-Optimal Adaptation in Metabolic Network Models.

Frontiers in molecular biosciences·2022
Same author

Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling.

Journal of theoretical biology·2020
Same author

Finding MEMo: minimum sets of elementary flux modes.

Journal of mathematical biology·2019
Same author

Formalizing Metabolic-Regulatory Networks by Hybrid Automata.

Acta biotheoretica·2019
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: May 14, 2026

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

Fast thermodynamically constrained flux variability analysis.

Arne C Müller1, Alexander Bockmayr

  • 1Department of Mathematics and Computer Science, Freie Universität Berlin, 14195 Berlin, Germany. arne.mueller@fu-berlin.de

Bioinformatics (Oxford, England)
|February 8, 2013
PubMed
Summary
This summary is machine-generated.

Flux variability analysis (FVA) with thermodynamic constraints (tFVA) can now be performed efficiently on genome-scale metabolic networks. The new Fast-tFVA algorithm significantly speeds up analysis and identifies thermodynamic inconsistencies in metabolic models.

More Related Videos

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Related Experiment Videos

Last Updated: May 14, 2026

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

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Flux variability analysis (FVA) is crucial for analyzing flux balance analysis (FBA) results in genome-scale metabolic networks.
  • Unbounded flux solutions in FBA can violate thermodynamic laws, necessitating the incorporation of thermodynamic constraints.
  • Extending FVA with thermodynamic constraints can resolve issues of unbounded fluxes in metabolic models.

Purpose of the Study:

  • To develop an efficient algorithm for flux variability analysis with thermodynamic constraints (tFVA) applicable to genome-scale metabolic networks.
  • To address the NP-hard nature of FBA with thermodynamic constraints by deriving a theoretical tractability result.
  • To identify previously undetected irreversible or fixed reactions in metabolic networks.

Main Methods:

  • Developed Fast-tFVA, a constraint programming algorithm for fast tFVA.
  • Derived a theoretical tractability result for FBA with thermodynamic constraints applicable to metabolic networks.
  • Implemented Fast-tFVA in C++ utilizing SCIP and libSBML, with a Matlab interface available.

Main Results:

  • Demonstrated the efficiency of Fast-tFVA through computational comparisons, achieving speed-ups of 30-300 times.
  • Showed that FBA with thermodynamic constraints is NP-hard but derived a practically applicable tractability result.
  • Identified additional irreversible or fixed reactions in 485 out of 716 genome-scale metabolic networks from the BioModels database.

Conclusions:

  • Fast-tFVA provides a significant computational advantage for performing tFVA on large-scale metabolic networks.
  • The developed method effectively detects thermodynamic inconsistencies and reveals constraints in metabolic models.
  • This approach enhances the accuracy and reliability of metabolic network analysis, aiding in metabolic engineering and systems biology research.