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: Distributed Parameter Models

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 Individual and Population Analysis

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)...

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the concentration...

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

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

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...

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...

Partial Fractions

Partial Fractions

A partial fraction is a component of a rational expression represented as the sum of simpler fractions. When a rational function is expressed as a ratio of two polynomials, it can often be decomposed into a sum of fractions whose denominators are simpler polynomials, typically linear or irreducible quadratic factors. This process is called partial fraction decomposition, and it is used to simplify complex expressions for integration, solving equations, or analysis.Partial fraction decomposition...

You might also read

Related Articles

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

Sort by

Same author

Two-dimensional Brownian motion with dependent components: Turning angle analysis.

Chaos (Woodbury, N.Y.)·2025

Same author

Langevin Equation in Heterogeneous Landscapes: How to Choose the Interpretation.

Physical review letters·2024

Same author

Erratum: "Fractional Brownian motion with random Hurst exponent: Accelerating diffusion and persistence transitions" [Chaos 32, 093114 (2022)].

Chaos (Woodbury, N.Y.)·2024

Same author

Confined modes of single-particle trajectories induced by stochastic resetting.

Physical review. E·2023

Same author

Testing of two-dimensional Gaussian processes by sample cross-covariance function.

Chaos (Woodbury, N.Y.)·2023

Same author

Goodness-of-fit test for stochastic processes using even empirical moments statistic.

Chaos (Woodbury, N.Y.)·2023

Search research articles

Related Experiment Video

Updated: May 16, 2026

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

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

Published on: September 26, 2016

Fractional process as a unified model for subdiffusive dynamics in experimental data.

Krzysztof Burnecki¹, Grzegorz Sikora, Aleksander Weron

¹Hugo Steinhaus Center, Institute of Mathematics and Computer Science, Wroclaw University of Technology, Wyspianskiego 27, 50-370 Wroclaw, Poland. krzysztof.burnecki@pwr.wroc.pl

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|December 11, 2012

Summary

This summary is machine-generated.

This study applies the fractional autoregressive integrated moving average (FARIMA) model to analyze subdiffusive dynamics in biological systems. The FARIMA model

More Related Videos

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Related Experiment Videos

Last Updated: May 16, 2026

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

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

Published on: September 26, 2016

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Area of Science:

Statistical modeling
Biophysics
Cell biology

Background:

Subdiffusive dynamics are prevalent in biological systems, including molecular motion within cells.
Understanding anomalous diffusion is crucial for comprehending cellular processes.

Purpose of the Study:

To demonstrate the utility of the fractional autoregressive integrated moving average (FARIMA) model for analyzing subdiffusive dynamics.
To apply the FARIMA(1,d,1) model to experimental data of molecular motion in live cells.

Main Methods:

Statistical analysis using a discrete time FARIMA(1,d,1) model.
Application to experimental data from E. coli mRNA molecule motion and human U2OS cancer cell telomere motion.

Main Results:

The memory parameter 'd' of the FARIMA model effectively detects anomalous dynamics in both experimental datasets.
The model's performance is independent of the distribution of random noises.

Conclusions:

The FARIMA model is a robust tool for characterizing anomalous diffusion in biological contexts.
The memory parameter 'd' is a key indicator of subdiffusive behavior in cellular environments.