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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

154
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...
154
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

365
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
365
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

353
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
353
Three-Compartment Open Model01:06

Three-Compartment Open Model

649
The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
649
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

166
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...
166
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.8K
The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
2.8K

You might also read

Related Articles

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

Sort by
Same author

A comprehensive pharmacological survey across heterogeneous patient-derived glioblastoma stem cell models.

iScience·2026
Same author

Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.

Digital discovery·2026
Same author

Multiobjective Design of Growth Media with Genome-Scale Metabolic Models and Bayesian Optimization.

Computational and structural biotechnology journal·2026
Same author

Predicting trajectories of illness using RNA velocity of whole blood.

Nature communications·2026
Same author

Synergy mediates long-range correlations in the visual cortex near criticality.

Frontiers in computational neuroscience·2026
Same author

Risk-averse optimization of genetic circuits under uncertainty.

Cell systems·2026

Related Experiment Video

Updated: Nov 22, 2025

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing
07:41

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Published on: February 4, 2017

8.7K

Computation of Single-Cell Metabolite Distributions Using Mixture Models.

Mona K Tonn1, Philipp Thomas1, Mauricio Barahona1

  • 1Department of Mathematics, Imperial College London, London, United Kingdom.

Frontiers in Cell and Developmental Biology
|January 8, 2021
PubMed
Summary

This study introduces a new method to predict metabolite distributions in single cells, explaining metabolic heterogeneity. It bridges deterministic and stochastic views of metabolism for disease insights.

Keywords:
metabolic modelingmetabolic variabilitymixture model analysissingle-cell modelingstochastic gene expression

More Related Videos

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry
11:39

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Published on: June 9, 2019

9.4K
Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage
08:07

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

Published on: September 16, 2025

805

Related Experiment Videos

Last Updated: Nov 22, 2025

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing
07:41

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Published on: February 4, 2017

8.7K
Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry
11:39

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Published on: June 9, 2019

9.4K
Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage
08:07

Sample Preparation for Single Cell Mass Spectrometry Metabolomics Studies: Combined Cell Washing, Quenching, Drying, and Storage

Published on: September 16, 2025

805

Area of Science:

  • Cellular metabolism
  • Systems biology
  • Biochemistry

Background:

  • Metabolic heterogeneity is a key challenge in understanding non-genetic variation.
  • Stochasticity of intracellular events is increasingly implicated in metabolic heterogeneity.
  • Metabolism has been traditionally modeled as deterministic, overlooking stochastic influences.

Purpose of the Study:

  • To develop a general method for predicting metabolite distributions across single cells.
  • To bridge the gap between deterministic and stochastic models of metabolism.
  • To provide a framework for understanding the molecular basis of metabolic heterogeneity.

Main Methods:

  • Exploiting the separation of time scales between enzyme expression and kinetics.
  • Developing Gaussian mixture models for metabolite distributions.
  • Computing distributions from single-cell expression data and deterministic metabolic models.

Main Results:

  • A general method for predicting metabolite distributions without lengthy simulations.
  • Metabolite distributions are modeled as Gaussian mixture models.
  • The method allows prediction of biochemical parameter impacts on distributions.

Conclusions:

  • The proposed method systematically predicts metabolite distributions.
  • It facilitates identification of molecular processes driving metabolic heterogeneity.
  • This work lays the groundwork for understanding functional implications in disease.