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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

158
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...
158
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

223
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
223
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

134
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...
134
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

331
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
331
Typical Model Studies01:30

Typical Model Studies

503
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.
503
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

175
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
175

You might also read

Related Articles

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

Sort by
Same author

Web-based collaborative model development in interdisciplinary consortia: Design principles and practical guidance.

PLoS biology·2026
Same author

MxlPy-Python package for mechanistic learning and hybrid modelling in life science.

Bioinformatics advances·2025
Same author

Integrative Thermodynamic Strategies in Microbial Metabolism.

International journal of molecular sciences·2025
Same author

Helixer: ab initio prediction of primary eukaryotic gene models combining deep learning and a hidden Markov model.

Nature methods·2025
Same author

Algebraic differentiation for fast sensitivity analysis of optimal flux modes in metabolic models.

Bioinformatics (Oxford, England)·2025
Same author

New avenues in photosynthesis: from light harvesting to global modeling.

Physiologia plantarum·2025
Same journal

OpenIMC: an open-source platform for analyzing single-cell and spatial proteomics by imaging mass cytometry.

BMC bioinformatics·2026
Same journal

NAP: an open source pipeline for cross-domain microbiome profiling using Nanopore sequencing-derived amplicon data.

BMC bioinformatics·2026
Same journal

SurvGME: an R package for survival analysis with graphical and measurement error models.

BMC bioinformatics·2026
Same journal

SimMapNet: a Bayesian framework for gene regulatory network inference using gene ontology similarities as external hint.

BMC bioinformatics·2026
Same journal

Dual channel drug-drug interactions extraction based on cross attention.

BMC bioinformatics·2026
Same journal

FeSseqdb: a curated sequence-level database and interpretable machine learning framework for identifying iron-sulfur proteins.

BMC bioinformatics·2026
See all related articles

Related Experiment Video

Updated: Nov 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Constructing and analysing dynamic models with modelbase v1.2.3: a software update.

Marvin van Aalst1, Oliver Ebenhöh1,2, Anna Matuszyńska3,4

  • 1Institute of Quantitative and Theoretical Biology, Heinrich Heine University, Universitätsstr. 1, 40225, Düsseldorf, Germany.

BMC Bioinformatics
|April 21, 2021
PubMed
Summary
This summary is machine-generated.

modelbase is a free Python package for building and analyzing mathematical models in systems biology and medicine. It enhances model reproducibility and allows for isotopic labeling studies, promoting collaborative research.

Keywords:
Biomedical systemsFlux analysisIsotope tracingLabellingMathematical modellingMetabolic networksODEResearch softwareSystems biologySystems medicine

More Related Videos

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.9K

Related Experiment Videos

Last Updated: Nov 8, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.9K

Area of Science:

  • Systems Biology
  • Systems Medicine
  • Computational Biology

Background:

  • Mathematical models are crucial for understanding biological systems, but their reuse is hindered by poor documentation and proprietary software.
  • Developing open-source tools is essential for consistent, transparent, and reproducible model construction in systems biology and medicine.

Purpose of the Study:

  • To introduce modelbase, an expandable Python package for constructing and analyzing ordinary differential equation-based mathematical models.
  • To enhance model reusability, transparency, and reproducibility in systems biology and medicine.

Main Methods:

  • Utilizing Python for an open-source toolbox to create and solve systems of ordinary differential equations.
  • Implementing unified methods for model construction and analysis, including expanded visualization tools.
  • Ensuring compatibility with Systems Biology Markup Language (SBML) and providing a library of common kinetic rate laws.

Main Results:

  • modelbase offers intuitive methods for constructing and solving dynamic systems, with enhanced visualization for structural and dynamic properties.
  • The package automatically assembles differential equations from specified stoichiometries and rate laws.
  • modelbase v1.2.3 streamlines the creation of isotope-specific models using user-provided label maps, aiding in isotope labeling studies.

Conclusions:

  • modelbase provides a consistent, tractable, and expandable platform for developing and replicating mathematical models.
  • The software facilitates the simulation of label propagation in isotopic labeling studies, offering quantitative insights into network topology and metabolic fluxes.
  • modelbase promotes collaborative research by making models transparent, reusable, and unified, with a continuously growing library of examples.