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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

333
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...
333
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

490
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
490
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

438
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...
438
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

1.5K
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...
1.5K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

727
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
727
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

359
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...
359

You might also read

Related Articles

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

Sort by
Same author

Molecular noise modulates transitions in the cell-fate differentiation landscape.

NPJ systems biology and applications·2026
Same author

PMGen: from peptide-MHC structure prediction to peptide generation.

Bioinformatics (Oxford, England)·2026
Same author

Fifty years since a simple equation described the chaos of biology.

Nature·2026
Same author

Stable β2-Microglobulin-HLA Class I Association Reshapes the Antigenic Landscape and TCR Recognition of Cancer-Associated Epitopes.

European journal of immunology·2026
Same author

OPN-Derived Peptides Generated by Proteasomes Can Promote Cell Migration via CD44 Activation.

Journal of immunology research·2026
Same author

Negative Selection Maintains Grossly Altered but Broadly Stable Karyotypes in Metastatic Colorectal Cancer.

Cancer discovery·2026
Same journal

High-throughput measurements of protein domain functions using magnetic separation.

Nature protocols·2026
Same journal

Inducing physiological polarity and performing gene editing using CRISPR-Cas9 in human trophoblast organoids.

Nature protocols·2026
Same journal

Photocatalytic low-temperature defluorination of PTFE.

Nature protocols·2026
Same journal

Multimodal imaging and quantification of lanthanide chelate-labeled micro- and nanoplastics in plants.

Nature protocols·2026
Same journal

Facilitating structure-based drug discovery with an artificial intelligence-driven virtual screening platform.

Nature protocols·2026
Same journal

Yeast nuclei-mediated precise delivery of synthetic megabase-scale human DNA into mammalian embryos.

Nature protocols·2026
See all related articles

Related Experiment Video

Updated: May 3, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

840

A framework for parameter estimation and model selection from experimental data in systems biology using approximate

Juliane Liepe1, Paul Kirk1, Sarah Filippi1

  • 1Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College, London, UK.

Nature Protocols
|January 25, 2014
PubMed
Summary
This summary is machine-generated.

Researchers can now calibrate complex biological models using ABC-SysBio, a new Python package. This tool aids in parameter estimation and model selection for stochastic systems, improving data analysis in life sciences.

More Related Videos

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.1K

Related Experiment Videos

Last Updated: May 3, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

840
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

10.6K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.1K

Area of Science:

  • Computational Biology
  • Systems Biology
  • Biomedical Informatics

Background:

  • Modeling is increasingly vital in life and biomedical sciences.
  • Researchers require robust tools for model calibration against complex data.
  • Existing tools may not adequately address stochastic model fitting.

Purpose of the Study:

  • Introduce ABC-SysBio, a Python-based Approximate Bayesian Computation (ABC) framework.
  • Enable parameter estimation and model selection using sequential Monte Carlo (SMC) methods.
  • Provide a practical guide for applying ABC-SysBio in biological research.

Main Methods:

  • Developed ABC-SysBio, a Python package for Linux and Mac OS X.
  • Utilized sequential Monte Carlo (SMC) approaches for Bayesian inference.
  • Demonstrated application using a simulated seven-reaction biological network.

Main Results:

  • ABC-SysBio successfully performed parameter inference for a biological reaction network.
  • The software effectively discriminated between different reaction network models.
  • Highlighted the suitability of ABC-SysBio for fitting stochastic models to data.

Conclusions:

  • ABC-SysBio offers a powerful framework for Bayesian analysis in systems biology.
  • The tool is particularly valuable for complex parameter estimation and model selection.
  • Addresses the challenge of fitting stochastic models, enhancing biological data analysis.