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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Distribution and Dispersion

Distribution and Dispersion

Ecology is the study of how organisms interact with their environment and with one another. An important aspect of ecology is understanding where species are found and how individuals are distributed within those areas. The geographic range of a species refers to the total area where its members are located, while dispersion describes the pattern of spacing of individuals within that range.Geographic Range and Dispersion PatternsWithin a species’ geographic range, individuals may be distributed...

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)...

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

Modeling with Differential Equations

Modeling with Differential Equations

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...

You might also read

Related Articles

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

Sort by

Same author

Stressed tumors and their maths: Comment on "Mechanotransduction in tumor dynamics modeling" by B. Blanco et al.

Physics of life reviews·2023

Same author

Data driven modeling of pseudopalisade pattern formation.

Journal of mathematical biology·2023

Same author

Feasibility and clinical usefulness of modelling glioblastoma migration in adjuvant radiotherapy.

Zeitschrift fur medizinische Physik·2021

Same author

Multiscale modeling of glioma pseudopalisades: contributions from the tumor microenvironment.

Journal of mathematical biology·2021

Same author

Nonlocal and local models for taxis in cell migration: a rigorous limit procedure.

Journal of mathematical biology·2020

Same author

Mathematical models for cell migration: a non-local perspective.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2020

Search research articles

Related Experiment Video

Updated: Jun 1, 2026

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

Modeling and simulation of some cell dispersion problems by a nonparametric method.

Christina Surulescu¹, Nicolae Surulescu

¹ICAM, WWU Münster, Münster, Germany. christina.surulescu@uni-muenster.de

Mathematical Biosciences and Engineering : MBE

|June 3, 2011

Summary

We developed new models for cell motion, incorporating random switching and obstacle avoidance for enhanced realism. Our nonparametric technique numerically assesses complex cell population behaviors, offering a unique solution for realistic modeling.

More Related Videos

Adapting Taylor Dispersion to Measure the Dispersion Coefficient of Electrolyte Solutions via an Accessible Microfluidic Setup

Adapting Taylor Dispersion to Measure the Dispersion Coefficient of Electrolyte Solutions via an Accessible Microfluidic Setup

Published on: October 7, 2025

Dispersion of Nanomaterials in Aqueous Media: Towards Protocol Optimization

Dispersion of Nanomaterials in Aqueous Media: Towards Protocol Optimization

Published on: December 25, 2017

Related Experiment Videos

Last Updated: Jun 1, 2026

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

Published on: February 23, 2018

Adapting Taylor Dispersion to Measure the Dispersion Coefficient of Electrolyte Solutions via an Accessible Microfluidic Setup

Adapting Taylor Dispersion to Measure the Dispersion Coefficient of Electrolyte Solutions via an Accessible Microfluidic Setup

Published on: October 7, 2025

Dispersion of Nanomaterials in Aqueous Media: Towards Protocol Optimization

Dispersion of Nanomaterials in Aqueous Media: Towards Protocol Optimization

Published on: December 25, 2017

Area of Science:

Mathematical Biology
Biophysics
Computational Biology

Background:

Classical cell motion models lack realism.
Complex behaviors like random motion switching and obstacle avoidance are challenging to model.

Purpose of the Study:

To propose enhanced models for cell motion realism.
To develop numerical methods for assessing complex cell population dynamics.

Main Methods:

Introduced new mathematical models for cell motility.
Utilized a nonparametric estimation technique for numerical assessment.
Incorporated features such as random switching between biased and unbiased motion and obstacle avoidance.

Main Results:

The proposed models enhance the realism of cell motion simulations.
The nonparametric technique successfully handles complex cell population behaviors.
This method is uniquely capable of numerically assessing such complex settings.

Conclusions:

The developed models and numerical techniques offer a significant advancement in simulating realistic cell motion.
This approach provides a robust framework for studying complex multicellular behaviors.