Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

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...

You might also read

Related Articles

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

Sort by

Same author

tubb5 knockout in zebrafish causes neurodevelopmental defects via notch pathways.

Human molecular genetics·2026

Same author

Extrachromosomal DNA replication and maintenance couple with DNA damage pathway in tumors.

Cell·2025

Same author

COCOA: A Framework for Fine-scale Mapping of Cell-type-specific Chromatin Compartments Using Epigenomic Information.

Genomics, proteomics & bioinformatics·2024

Same author

HTAD: a human-in-the-loop framework for supervised chromatin domain detection.

Genome biology·2024

Same author

A new clinical age of aging research.

Trends in endocrinology and metabolism: TEM·2024

Same author

Liebig's law of the minimum in the TGF-β/SMAD pathway.

PLoS computational biology·2024

Search research articles

Related Experiment Video

Updated: Jun 4, 2026

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

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

Published on: February 8, 2017

SBML-PET-MPI: a parallel parameter estimation tool for Systems Biology Markup Language based models.

¹BIOSS Centre for Biological Signalling Studies, Center for Biological Systems Analysis (ZBSA), University of Freiburg, Freiburg, Germany. zhike.zi@bioss.uni-freiburg.de

Bioinformatics (Oxford, England)

|February 10, 2011

Summary

This study introduces SBML-PET-MPI, a parallel tool for Systems Biology Markup Language (SBML) model parameter estimation. It speeds up dynamic analysis and parameter uncertainty analysis for biological systems.

More Related Videos

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

Published on: May 15, 2019

Related Experiment Videos

Last Updated: Jun 4, 2026

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

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

Published on: February 8, 2017

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

Using the Open-Source MALDI TOF-MS IDBac Pipeline for Analysis of Microbial Protein and Specialized Metabolite Data

Published on: May 15, 2019

Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Parameter estimation is vital for biological system modeling and dynamic analysis.
Current methods are often time-consuming and computationally intensive.
Systems Biology Markup Language (SBML) is a standard for representing biological models.

Purpose of the Study:

To introduce a novel parallel parameter estimation tool for SBML-based models.
To enhance the efficiency of parameter estimation and uncertainty analysis.
To facilitate the collective fitting of multiple experimental datasets.

Main Methods:

Development of a parallel parameter estimation tool named SBML-PET-MPI.
Implementation using the Message Passing Interface (MPI) protocol for parallelization.
Integration of parameter estimation and parameter uncertainty analysis functionalities.

Main Results:

SBML-PET-MPI enables efficient parameter estimation for SBML models.
The tool demonstrates good scalability with increasing processor numbers.
It supports collective fitting of multiple experimental datasets, improving analysis.
Parameter uncertainty analysis is integrated into the workflow.

Conclusions:

SBML-PET-MPI significantly reduces the computational burden of parameter estimation.
The tool enhances the dynamic analysis of biological systems.
It offers a scalable and efficient solution for systems biology research.