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

104
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...
104
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

117
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...
117
Entropy within the Cell01:22

Entropy within the Cell

11.9K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
11.9K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.8K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.8K
Genetic Drift03:33

Genetic Drift

41.3K
Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
41.3K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

425
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...
425

You might also read

Related Articles

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

Sort by
Same author

Beyond Mendel: a call to revisit the genotype-phenotype map through new experimental paradigms.

Genetics·2026
Same author

[Congenital epulis].

Orvosi hetilap·2026
Same author

Genealogical Analysis of Replicate Flower Colour Hybrid Zones in Antirrhinum.

Molecular ecology·2025
Same author

Joint Estimation of Paternity, Sibships and Pollen Dispersal in a Snapdragon Hybrid Zone.

Molecular ecology·2025
Same author

An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images.

Journal of medical systems·2024
Same author

How chromosomal inversions reorient the evolutionary process.

Journal of evolutionary biology·2023

Related Experiment Video

Updated: Oct 11, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K

Dynamic maximum entropy provides accurate approximation of structured population dynamics.

Katarína Bod'ová1, Enikő Szép2, Nicholas H Barton2

  • 1Faculty of Mathematics, Physics and Informatics, Comenius University, Bratislava, Slovakia.

Plos Computational Biology
|December 1, 2021
PubMed
Summary
This summary is machine-generated.

This study simplifies complex biological models using a dynamic maximum entropy method. The approach accurately captures macroscopic behaviors in changing environments, offering a powerful tool for analyzing stochastic processes.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

2.3K

Related Experiment Videos

Last Updated: Oct 11, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Study of Protein Dynamics via Neutron Spin Echo Spectroscopy
08:03

Study of Protein Dynamics via Neutron Spin Echo Spectroscopy

Published on: April 13, 2022

2.3K

Area of Science:

  • Computational Biology
  • Mathematical Modeling
  • Theoretical Ecology

Background:

  • Realistic biological models involve multi-scale interactions, environmental changes, and stochasticity, often leading to analytical and numerical intractability.
  • Existing methods struggle with the complexity of non-equilibrium dynamics and microscopic details.

Purpose of the Study:

  • To explain and understand a dynamic maximum entropy method for simplifying stochastic non-equilibrium dynamics.
  • To demonstrate the method's accuracy in capturing macroscopic quantities under non-stationary conditions.

Main Methods:

  • Combines static maximum entropy with a quasi-stationary approximation.
  • Reduces Fokker-Planck equations to low-dimensional deterministic dynamics.
  • Applies the method to the Ornstein-Uhlenbeck process and a stochastic island model.

Main Results:

  • The method accurately recovers exact dynamics for the Ornstein-Uhlenbeck process.
  • High macroscopic accuracy is maintained for a stochastic island model in dynamic environments.
  • Effectively captures key macroscopic quantities despite rapid environmental changes.

Conclusions:

  • The dynamic maximum entropy method provides an efficient and accurate approach for analyzing complex stochastic biological systems.
  • This simplification is valuable for understanding population genetics and ecological dynamics in fluctuating environments.