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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

288
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
288
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.2K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.2K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

355
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...
355
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

621
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
621
Typical Model Studies01:30

Typical Model Studies

644
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
644
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.1K
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Assessment of simulation-based inference methods for stochastic compartmental models in epidemiological research.

PloS one·2026
Same author

Injury prediction in elite women's football: an integrative machine learning-based decision-support framework.

NPJ digital medicine·2026
Same author

Longitudinal spatial neutrophil profiling during ACT in murine melanoma reveals distinct lymph node infiltration patterns.

NPJ systems biology and applications·2026
Same author

Data-driven model reveals increased stability of CAG-expanded huntingtin RNA due to MID1 binding.

PLoS computational biology·2026
Same author

Homing pigeon navigation relies on superparamagnetic macrophages under overcast conditions.

Science (New York, N.Y.)·2026
Same author

Suggested experimental design and computational modeling to infer single-cell lipid dynamics from a single destructive measurement.

iScience·2026

Related Experiment Video

Updated: Feb 17, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K

GenSSI 2.0: multi-experiment structural identifiability analysis of SBML models.

Thomas S Ligon1, Fabian Fröhlich2,3, Oana T Chis4

  • 1Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, 80539 München, Germany.

Bioinformatics (Oxford, England)
|December 6, 2017
PubMed
Summary

GenSSI 2.0 enhances structural identifiability analysis for ordinary differential equation models in systems biology. This open-source MATLAB toolbox improves computational efficiency and supports multi-experiment analysis for complex biological models.

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.4K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.4K

Related Experiment Videos

Last Updated: Feb 17, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.0K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.4K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.4K

Area of Science:

  • Systems Biology
  • Computational Biology

Background:

  • Mathematical modeling with ordinary differential equations is crucial for understanding dynamic biological processes.
  • Parameter estimation from experimental data is essential for these models.
  • Structural identifiability analysis methods are needed to assess the uniqueness of parameter estimation solutions.

Purpose of the Study:

  • Introduce GenSSI 2.0, an advanced software toolbox for structural identifiability analysis.
  • Provide a tool that imports Systems Biology Markup Language, handles state/parameter transformations, and supports multi-experiment analysis.
  • Enhance computational efficiency for analyzing more complex ordinary differential equation models.

Main Methods:

  • GenSSI 2.0 implements advanced algorithms for structural identifiability analysis.
  • The toolbox supports Systems Biology Markup Language (SBML) model import.
  • It incorporates functionality for state and parameter transformations and multi-experiment analysis.

Main Results:

  • GenSSI 2.0 is the first toolbox to offer SBML import for structural identifiability analysis.
  • The new version is computationally more efficient, allowing analysis of larger and more complex models.
  • It supports multi-experiment analyses, providing a more comprehensive assessment of model identifiability.

Conclusions:

  • GenSSI 2.0 significantly advances the capabilities for structural identifiability analysis in systems biology.
  • The toolbox facilitates more robust and efficient analysis of ordinary differential equation models.
  • Its open-source nature and enhanced features promote wider adoption and application in biological research.