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

250
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...
250
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

292
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...
292
Modeling with Differential Equations01:25

Modeling with Differential Equations

21
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...
21
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

280
Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
280
Typical Model Studies01:30

Typical Model Studies

620
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
620
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

503
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,...
503

You might also read

Related Articles

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

Sort by
Same author

Roadmap for animate matter.

Journal of physics. Condensed matter : an Institute of Physics journal·2025
Same author

A neuromorphic model of active vision shows how spatiotemporal encoding in lobula neurons can aid pattern recognition in bees.

eLife·2025
Same author

Active vision of bees in a simple pattern discrimination task.

eLife·2025
Same author

Coherent movement of error-prone individuals through mechanical coupling.

Nature communications·2023
Same author

How honey bees make fast and accurate decisions.

eLife·2023
Same author

Asynchrony rescues statistically optimal group decisions from information cascades through emergent leaders.

Royal Society open science·2023

Related Experiment Video

Updated: Jan 18, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K

Multiscale Modelling Tool: Mathematical modelling of collective behaviour without the maths.

James A R Marshall1, Andreagiovanni Reina1, Thomas Bose1

  • 1Department of Computer Science, University of Sheffield, Sheffield, United Kingdom.

Plos One
|October 1, 2019
PubMed
Summary

This study introduces an accessible tool to automate the analysis of collective behaviour models. It simplifies complex mathematical techniques, making collective behaviour modeling easier for researchers across various scientific disciplines.

More Related Videos

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans
03:32

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Published on: August 25, 2023

1.5K
3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K

Related Experiment Videos

Last Updated: Jan 18, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.8K
Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans
03:32

Author Spotlight: Collective Behavioral Analysis of the Nematode, Caenorhabditis elegans

Published on: August 25, 2023

1.5K
3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.3K

Area of Science:

  • Collective behaviour spans life sciences, social sciences, and physical/engineering sciences.
  • It involves complex emergent dynamics arising from non-linear interactions.
  • Understanding collective behaviour is crucial for scientific progress and technological design.

Background:

  • Analyzing collective behaviour is challenging due to complex dynamics and demanding mathematical techniques.
  • Existing methods require extensive training, are time-consuming, and prone to errors.
  • This creates a barrier for practitioners seeking to study collective behaviour models.

Purpose of the Study:

  • To present an accessible tool for automating the modeling and analysis of collective behaviour.
  • To simplify the application of advanced mathematical techniques for a wider audience.
  • To facilitate sophisticated investigations and advance standards in collective behaviour modeling.

Main Methods:

  • Focuses on systems described by reaction kinetics, involving state-changing component interactions.
  • Automates access to advanced mathematical techniques from statistical physics and nonlinear dynamical systems analysis.
  • Incorporates computational simulation and provides interactive graphical plots.

Main Results:

  • The tool offers automated modeling and analysis of collective behaviour.
  • It simplifies complex mathematical techniques, lowering the barrier to entry for researchers.
  • Expert users benefit from facilitated sophisticated investigations and automated analysis results.

Conclusions:

  • The developed tool democratizes the analysis of collective behaviour.
  • It enhances understanding and application of collective behaviour across disciplines.
  • The online, accessible platform promotes broader engagement with complex modeling.