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: Physiological Models

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: 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...

Pharmacokinetic Models: Overview

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,...

Pharmacodynamic Models: Overview

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...

You might also read

Related Articles

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

Sort by

Same author

Effect of coil geometry on sensitivity distribution in field-free line magnetic particle imaging.

Scientific reports·2026

Same author

Transient outward K+ current inhibition suppresses early afterdepolarizations and ventricular arrhythmias via trajectory displacement away from an oscillatory regime.

Chaos (Woodbury, N.Y.)·2026

Same author

Modulation of prefrontal functional connectivity by tDCS over the left DLPFC predicts performance enhancement in swimmers: a simultaneous tDCS-fNIRS, double-blind, sham-controlled crossover study.

European journal of applied physiology·2026

Same author

The effects of voltage dependence and ion-binding reaction rates on a thermodynamically constrained mathematical model of the Na/Ca exchanger.

Scientific reports·2025

Same author

Analysis of the relationship between rod cell membrane currents and the photoreceptor component of electroretinograms using a cable model.

Scientific reports·2025

Same author

Multimodal Imaging to Identify Brain Markers of Human Prosocial Behavior.

eNeuro·2025

Search research articles

Related Experiment Video

Updated: Jul 10, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

A generic representation format of physiological experimental protocols for computer simulation using ontology.

Takao Shimayoshi¹, Akira Amano, Tetsuya Matsuda

¹ASTEM Research Insutitute of Kyoto, Kyoto, 600-8813, Japan. simayosi@astem.or.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

Summary

We developed Physiological Experimental Protocol Markup Language (PEPML), an XML-based format for describing physiological experiment protocols. PEPML enables efficient, reusable, and adaptable computer simulations for physiological models.

Related Experiment Videos

Last Updated: Jul 10, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Area of Science:

Computational Biology
Physiological Modeling
Bioinformatics

Background:

Computer simulations of physiological experiments require machine-readable experimental protocols.
Existing methods lack standardization and flexibility for diverse physiological models.

Purpose of the Study:

To introduce an XML-based language, Physiological Experimental Protocol Markup Language (PEPML), for describing physiological experimental protocols.
To enhance the efficiency and reusability of computer simulations for physiological models.

Main Methods:

Developed PEPML, an XML-based language defining protocols as procedural events with conditions and actions.
Integrated ontology support for specifying variables, enabling model adaptability without editing.
Designed PEPML for flexible application of multiple protocols to single models and vice versa.

Main Results:

PEPML provides a standardized, machine-readable format for physiological experimental protocols.
The language facilitates the application of protocols across various physiological models.
Enables efficient simulations for model verification, comparison, and utilization.

Conclusions:

PEPML offers a flexible and efficient solution for computer simulations in physiological research.
The proposed language enhances the interoperability and reusability of physiological models.
PEPML supports streamlined simulation workflows for advancing physiological understanding.