Related Experiment Video

Updated: Aug 15, 2026

A Visual Guide for Studying Behavioral Defenses to Pathogen Attacks in Leaf-Cutting Ants
08:10

A Visual Guide for Studying Behavioral Defenses to Pathogen Attacks in Leaf-Cutting Ants

Published on: October 12, 2018

Ants and agents: a process algebra approach to modelling ant colony behaviour

D J Sumpter1, G B Blanchard, D S Broomhead

  • 1Centre for Mathematical Biology, Mathematical Institute, 24-29 St Giles', Oxford OX1 3LB, U.K. sumpter@maths.ox.ac.uk

Bulletin of Mathematical Biology
|September 22, 2001
PubMed

More Related Videos

The HoneyComb Paradigm for Research on Collective Human Behavior06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Collection and Long-Term Maintenance of Leaf-Cutting Ants (Atta) in Laboratory Conditions10:11

Collection and Long-Term Maintenance of Leaf-Cutting Ants (Atta) in Laboratory Conditions

Published on: August 30, 2022

Related Concept Videos

Biot-Savart Law: Problem-Solving00:59

Biot-Savart Law: Problem-Solving

The magnitude and direction of a magnetic field created by a steady current can be calculated using the Biot-Savart law.
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
Principle of Moments: Problem Solving01:30

Principle of Moments: Problem Solving

The principle of moments is a fundamental concept in physics and engineering. It refers to the balancing of forces and moments around a point or axis, also known as the pivot. This principle is used in many real-life scenarios, including construction, sports, and daily activities like opening doors and pushing objects.
One such scenario involves a pole placed in a three-dimensional system with a cable attached. When a tension is applied to the cable, the moment about the z-axis passing through...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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

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...
Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

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,...