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

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

378
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
378
Modeling with Differential Equations01:25

Modeling with Differential Equations

117
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
117
Diffusion01:12

Diffusion

223.1K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
223.1K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

280
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...
280
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

614
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,...
614
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

290
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...
290

You might also read

Related Articles

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

Sort by
Same author

A Randomized Phase II Study of MEDI0680 in Combination with Durvalumab versus Nivolumab Monotherapy in Patients with Advanced or Metastatic Clear-cell Renal Cell Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2022
Same author

Transcriptomics reveals complex kinetics of dorsal-ventral patterning gene expression in the mandibular arch.

Genesis (New York, N.Y. : 2000)·2018
See all related articles

Related Experiment Video

Updated: Feb 22, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.8K

Multiscale Modeling of Diffusion in a Crowded Environment.

Lina Meinecke1,2

  • 1Department of Information Technology, Uppsala University, Uppsala, Sweden. lina.meinecke@uci.edu.

Bulletin of Mathematical Biology
|September 20, 2017
PubMed
Summary

Molecular crowding significantly impacts diffusion and reaction rates in biological systems. Our multiscale model reveals how obstacle density and molecular size influence these processes, affecting cellular chemistry.

Keywords:
Macromolecular crowdingStochastic reaction–diffusion simulations

More Related Videos

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
11:34

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

Published on: September 8, 2016

10.8K
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K

Related Experiment Videos

Last Updated: Feb 22, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

8.8K
Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels
11:34

Controlled Synthesis and Fluorescence Tracking of Highly Uniform PolyN-isopropylacrylamide Microgels

Published on: September 8, 2016

10.8K
Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level
06:55

Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

8.5K

Area of Science:

  • Biophysics
  • Computational Biology
  • Chemical Kinetics

Background:

  • Diffusion in biological systems is crucial for cellular processes.
  • Modeling diffusion requires accounting for inherent biological noise, especially at low molecular counts.
  • The crowded cellular environment presents challenges for accurate diffusion modeling.

Purpose of the Study:

  • To develop a multiscale approach for modeling diffusion in crowded cellular environments.
  • To investigate the effects of molecular crowding on reaction rates.
  • To understand how excluded volume effects alter diffusive motion.

Main Methods:

  • A mesoscopic model using discrete space jump processes was employed.
  • Jump rates were computed from local first exit times, incorporating microscopic obstacle positions.
  • A macroscopic diffusion equation with a space-dependent diffusion coefficient was derived.

Main Results:

  • The multiscale model efficiently captures diffusion in crowded environments.
  • Crowding molecules' shape, size, and the diffusing molecule's size critically affect diffusion.
  • Molecular crowding can either enhance or inhibit chemical reactions based on local obstacle density fluctuations.

Conclusions:

  • The developed multiscale approach accurately models diffusion and its impact on reaction rates in crowded biological systems.
  • Understanding excluded volume effects is key to predicting cellular reaction dynamics.
  • Local variations in obstacle density play a significant role in modulating biochemical reaction outcomes.