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

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 Solving01:29

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...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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 Models01:21

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

Pharmacokinetic Models: Comparison and Selection Criterion

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.

You might also read

Related Articles

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

Sort by
Same author

The impact of facial expressions on space- and object-based attention by gaze cues.

Cognitive processing·2026
Same author

Rapid reduction in global chromatin loop size after acute STAG2 reconstitution in human cancer cells.

The Journal of biological chemistry·2026
Same author

Research Progress on Anti-Inflammatory Adipokine SFRP5-Mediated Lipid Metabolism and Its Potential Role in Neural Development.

Immunity, inflammation and disease·2026
Same author

An Engineering Perspective on the Importance of Obtaining Operational Stability in Graduate School.

BioEssays : news and reviews in molecular, cellular and developmental biology·2026
Same author

Rapid reduction in global chromatin loop size after acute STAG2 reconstitution in human cancer cells.

bioRxiv : the preprint server for biology·2026
Same author

Photothermal PCR within 6 min based on pullulan-coated nanoplasmas for ultrafast nucleic acid analysis.

Analytica chimica acta·2026
Same journal

Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice.

BMC systems biology·2019
Same journal

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.

BMC systems biology·2019
Same journal

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.

BMC systems biology·2019
Same journal

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

BMC systems biology·2019
Same journal

GNE: a deep learning framework for gene network inference by aggregating biological information.

BMC systems biology·2019
Same journal

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

BMC systems biology·2019
See all related articles

Related Experiment Video

Updated: May 20, 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

Bayesian parameter estimation for nonlinear modelling of biological pathways.

Omid Ghasemi1, Merry L Lindsey, Tianyi Yang

  • 1Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX, USA.

BMC Systems Biology
|July 13, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian algorithm for estimating parameters in complex biological models. The method effectively analyzes time-series data from biological pathways, proving useful for systems biology research.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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

Related Experiment Videos

Last Updated: May 20, 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

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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

Area of Science:

  • Systems Biology
  • Computational Biology
  • Mathematical Modeling

Background:

  • Temporal measurements are crucial for understanding biological systems.
  • Ordinary differential equations and Hill equations model biological pathways but present parameter estimation challenges due to nonlinearity.
  • Existing adaptive estimation algorithms are unsuitable for nonlinear models.

Purpose of the Study:

  • To develop a Bayesian parameter estimation algorithm for nonlinear mathematical models of biological pathways.
  • To address the challenges of parameter estimation in complex, nonlinear biological systems using time-series data.

Main Methods:

  • Transformed differential equations to difference equations using the Runge-Kutta method.
  • Applied a Bayesian approach, specifically the Markov chain Monte Carlo (MCMC) method, for parameter estimation.
  • Validated the algorithm on biological pathways related to left ventricle response to myocardial infarction.

Main Results:

  • The proposed Bayesian algorithm successfully estimated parameters in nonlinear mathematical models.
  • Demonstrated effectiveness for both linearly and nonlinearly parameterized dynamic systems across various settings.
  • Verified algorithm performance by estimating parameters in a Hill equation within a myocardial infarction model.

Conclusions:

  • The Bayesian algorithm provides a robust method for parameter estimation in nonlinear biological models.
  • This approach can be extended to higher-order systems, aiding the analysis of biological dynamics.
  • Offers a valuable tool for extracting information from temporal biological data.