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

393
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...
393
Introduction to MATLAB01:24

Introduction to MATLAB

1.1K
MATLAB stands for Matrix Laboratory. MathWorks developed MATLAB as a multi-paradigm numerical computing environment and proprietary programming language. It has evolved significantly over the years to become a tool utilized by engineers, scientists, and mathematicians for various tasks, including matrix calculations, developing algorithms, data analysis, and visualization. MATLAB's applications span various industries and disciplines. It's used in image and signal processing,...
1.1K
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

512
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
512
Modeling with Differential Equations01:25

Modeling with Differential Equations

164
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
164
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

3.8K
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
3.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

321
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...
321

You might also read

Related Articles

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

Sort by
Same author

Advancing mechanobiology from single molecules to complex cellular systems.

Nature nanotechnology·2026
Same author

MorphoGrad: A MATLAB toolbox for simulating steady-state morphogen gradients under cell-to-cell variability.

STAR protocols·2026
Same author

Consensus Pituitary Atlas, a scalable resource for annotation, novel marker discovery, and analyses in mouse pituitary gland research.

Cell reports·2026
Same author

Multi-Omic, Multi-Tissue Responses to Acute Exercise in Sedentary Adults: Findings from the Molecular Transducers of Physical Activity Consortium.

bioRxiv : the preprint server for biology·2026
Same author

Redundant and distinct mechanisms suppress innate immune activation during SARS-CoV-2 infection.

PLoS biology·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026
Same journal

conMItion: an R package adjusting confounding factors for associations in multi-omics.

Bioinformatics (Oxford, England)·2026
Same journal

SpaMFG: a Spatial Multi-omics Integration Method based on Feature Grouping.

Bioinformatics (Oxford, England)·2026
Same journal

CSCN: Inference of Cell-Specific Causal Networks Using Single-Cell RNA-Seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

Sparse CCA-Based Mediation Analysis with High-Dimensional Exposures and Mediators.

Bioinformatics (Oxford, England)·2026
Same journal

Enhancing Cross-Context Generalization in Drug Perturbation Prediction with a Multimodal Conditional Diffusion Framework.

Bioinformatics (Oxford, England)·2026
Same journal

Primer Design through Submodular Function Estimation.

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

Related Experiment Video

Updated: Mar 26, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.7K

MOCCASIN: converting MATLAB ODE models to SBML.

Harold F Gómez1, Michael Hucka2, Sarah M Keating3

  • 1Department of Biosystems Science and Engineering, ETH Zürich, Basel CH 4058, Switzerland.

Bioinformatics (Oxford, England)
|February 11, 2016
PubMed
Summary
This summary is machine-generated.

We developed MOCCASIN to convert MATLAB ordinary differential equation (ODE) models into Systems Biology Markup Language (SBML) format, facilitating model sharing and interoperability in biological research.

More Related Videos

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

4.4K
In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology
12:58

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Published on: December 4, 2015

10.3K

Related Experiment Videos

Last Updated: Mar 26, 2026

A Web Tool for Generating High Quality Machine-readable Biological Pathways
08:01

A Web Tool for Generating High Quality Machine-readable Biological Pathways

Published on: February 8, 2017

18.7K
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

4.4K
In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology
12:58

In Situ Mapping of the Mechanical Properties of Biofilms by Particle-tracking Microrheology

Published on: December 4, 2015

10.3K

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • MATLAB is widely used in biological research for simulating models with ordinary differential equations (ODEs).
  • Sharing and reusing MATLAB models outside the MATLAB environment is often difficult.
  • Translating MATLAB models to a community standard like Systems Biology Markup Language (SBML) can be challenging, particularly for older models.

Purpose of the Study:

  • To develop a tool that automates the conversion of ODE-based MATLAB models to SBML.
  • To enhance interoperability and facilitate the sharing of biochemical reaction network models.

Main Methods:

  • Developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability).
  • MOCCASIN converts ODE-based MATLAB models into the SBML format.
  • The tool is designed to handle legacy models not originally intended for translation.

Main Results:

  • MOCCASIN successfully converts ODE-based MATLAB models of biochemical reaction networks into SBML.
  • This conversion facilitates the use and sharing of these models in a standardized format.

Conclusions:

  • MOCCASIN provides a solution for translating MATLAB ODE models to SBML.
  • The tool improves model interoperability and accessibility within the biological research community.