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

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

801
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
801
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

383
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
383

You might also read

Related Articles

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

Sort by
Same author

A computational framework for building dynamic bioreactor models integrating detailed enzyme kinetics with limited data: beta-ionone production in Saccharomyces cerevisiae as a case study.

Bioprocess and biosystems engineering·2026
Same author

Extracellular vesicle depletion improves transient gene expression performance and alters cellular transcriptional responses in CHO cell.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2026
Same author

Omics-guided engineering of CHO cells reveals host targets to support AAV production.

Molecular therapy. Advances·2026
Same author

Annotating the pangenome reveals the diversity in the genetic basis for metabolic enzymes.

Science advances·2026
Same author

A community reconstruction of Chinese hamster metabolism and structural systems biology elucidate metabolic rewiring in lactate-free CHO cells.

Cell systems·2026
Same author

The Role of Glycan Structures in Modulating GM-CSF Bioactivity: Insights from Glycoengineering.

bioRxiv : the preprint server for biology·2026

Related Experiment Video

Updated: Mar 15, 2026

Exploring Mitochondrial Energy Metabolism of Single 3D Microtissue Spheroids Using Extracellular Flux Analysis
08:15

Exploring Mitochondrial Energy Metabolism of Single 3D Microtissue Spheroids Using Extracellular Flux Analysis

Published on: February 3, 2022

3.7K

Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models.

Pedro A Saa1, Lars K Nielsen1

  • 1Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Corner College and Cooper Rds (Bldg 75), Australia.

Bioinformatics (Oxford, England)
|August 26, 2016
PubMed
Summary

A new algorithm, Fast-SNP, simplifies complex metabolic models by reducing loopless constraints. This makes large-scale metabolic flux optimization computationally feasible and efficient.

More Related Videos

Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes
06:26

Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes

Published on: March 28, 2025

1.1K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

4.3K

Related Experiment Videos

Last Updated: Mar 15, 2026

Exploring Mitochondrial Energy Metabolism of Single 3D Microtissue Spheroids Using Extracellular Flux Analysis
08:15

Exploring Mitochondrial Energy Metabolism of Single 3D Microtissue Spheroids Using Extracellular Flux Analysis

Published on: February 3, 2022

3.7K
Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes
06:26

Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes

Published on: March 28, 2025

1.1K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

4.3K

Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Flux balance analysis (FBA) is standard for metabolic modeling but requires loopless constraints for thermodynamic feasibility.
  • Loopless constraints transform the problem into a computationally expensive Mixed Integer Linear Programming (MILP) problem, hindering large-scale analysis.

Purpose of the Study:

  • To develop an efficient algorithm for simplifying loopless constraints in metabolic models.
  • To improve the computational tractability of large-scale metabolic flux optimization.

Main Methods:

  • Developed a pre-processing algorithm, Fast-sparse null-space pursuit (Fast-SNP), for matrix sparsification.
  • Applied Fast-SNP to reduce the size of loopless MILP problems while preserving equivalence.
  • Utilized Fast-SNP to identify key directional constraints for eliminating infeasible loops.

Main Results:

  • Fast-SNP significantly reduces the complexity of loopless MILP problems.
  • The algorithm improves computational efficiency for various metabolic models and loopless optimization tasks.
  • Fast-SNP enables the identification of constraints to permanently eliminate infeasible loops.

Conclusions:

  • Fast-SNP is an effective and simple algorithm for formulating loop-law constraints.
  • The method makes loopless flux optimization feasible and numerically tractable for large-scale metabolic models.
  • This advancement facilitates more efficient and accurate metabolic modeling and analysis.