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

Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal assumptions,...
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.
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...
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...
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...
Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs01:21

Fundamental Mathematical Principles in Pharmacokinetics: Calculus and Graphs

The fundamental mathematical principles, such as calculus and graphs, play crucial roles in analyzing drug movement and determining pharmacokinetic parameters. Differential calculus examines rates of change and helps to determine the dissolution rate of drugs in biofluids, as well as how drug concentrations change over time. For instance, it can help calculate the rate of elimination of a drug from the body based on its concentration-time profile.
On the other hand, integral calculus focuses on...

You might also read

Related Articles

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

Sort by
Same author

The Common Fund Data Ecosystem (CFDE).

bioRxiv : the preprint server for biology·2026
Same author

Ontology- and LLM-based data harmonization for federated learning in healthcare.

Frontiers in digital health·2026
Same author

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

NPJ systems biology and applications·2026
Same author

Investigating the effectiveness of mobilisation alarms to prevent hospital falls using disinvestment: A randomised clinical trial.

International journal of nursing studies·2025
Same author

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.

bioRxiv : the preprint server for biology·2025
Same author

Developing a multiscale neural connectivity knowledgebase of the autonomic nervous system.

Frontiers in neuroinformatics·2025
Same journal

Molecular Mechanisms of Cellulase Biosynthesis in Trichoderma reesei: Regulatory Networks, Secretion Pathways, and Environmental Modulation.

Biotechnology journal·2026
Same journal

The Impact of Collection Protocol on the Yield and Purity of Mesenchymal Stem Cell-Derived Extracellular Vesicles Isolated From Serum-Free Media.

Biotechnology journal·2026
Same journal

Biochemical and Functional Characterization of a Novel GH46 Chitosanase for Efficient Chitooligosaccharide Synthesis.

Biotechnology journal·2026
Same journal

LaeA Orchestrates Iron-Heme Supply and P450 Catalytic Efficiency for Enhanced Echinocandin B Biosynthesis in Aspergillus nidulans.

Biotechnology journal·2026
Same journal

Emerging Bioengineering Technologies in Female Reproduction: Preclinical Advances, Translational Challenges, and Future Outlook.

Biotechnology journal·2026
Same journal

Multi-Enzyme Cascade Reaction of Crude Enzyme Strategy for the Economical and Efficient Bioconversion of Rebaudioside A to Rebaudioside M.

Biotechnology journal·2026
See all related articles

Related Experiment Video

Updated: May 19, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Integrating knowledge representation and quantitative modelling in physiology.

Bernard de Bono1, Peter Hunter

  • 1Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.

Biotechnology Journal
|August 14, 2012
PubMed
Summary
This summary is machine-generated.

Computational physiology generates reusable data and models, but sharing is limited by inconsistent cataloguing. Applying community standards for model encoding and semantic annotation can enable automated integration for personalized medicine.

More Related Videos

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Related Experiment Videos

Last Updated: May 19, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

In Silico Clinical Trials for Cardiovascular Disease
09:09

In Silico Clinical Trials for Cardiovascular Disease

Published on: May 27, 2022

Area of Science:

  • Physiology
  • Computational Biology
  • Bioinformatics

Background:

  • Computational methods generate numerous physiological data and models.
  • Lack of standardized cataloguing and annotation hinders resource reusability and sharing.
  • Existing initiatives like the Physiome Project aim to integrate physiological models across scales.

Purpose of the Study:

  • To propose a vision for applying community-based modeling standards for automated integration of physiological models.
  • To highlight the role of model encoding and semantic metadata annotation in resource sharing.
  • To demonstrate how standards can support management, searching, and visualization of physiology models for healthcare decisions.

Main Methods:

  • Outlining a vision for community-based modeling standards.
  • Focusing on model encoding and semantic metadata annotation within the Physiome and Virtual Physiological Human Projects.
  • Illustrating applications with biomedical modeling scenarios (e.g., atrial natriuretic peptide, alcohol/glucose toxicity).

Main Results:

  • Standardized model encoding and semantic annotation are crucial for resource sharing.
  • Ontologies and knowledge representation approaches facilitate model management and integration.
  • These standards can provide a rational basis for healthcare decisions.

Conclusions:

  • Implementing community-based modeling standards is essential for unlocking the potential of computational physiology resources.
  • Automated integration of models across physiological systems and scales is achievable.
  • This approach supports personalized medicine by enabling better data and model utilization for healthcare.