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

Modeling with Differential Equations01:25

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...
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...
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)...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...

You might also read

Related Articles

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

Sort by
Same author

Molecular noise modulates transitions in the cell-fate differentiation landscape.

NPJ systems biology and applications·2026
Same author

Fifty years since a simple equation described the chaos of biology.

Nature·2026
Same author

Learning cell-specific networks from dynamics and geometry of single cells.

Cell systems·2025
Same author

Towards a mathematical framework for modelling cell fate dynamics.

Journal of mathematical biology·2025
Same author

The topological properties of the protein universe.

Nature communications·2025
Same author

Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems.

Annual review of biomedical data science·2025

Related Experiment Video

Updated: Jun 19, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Simulation-based model selection for dynamical systems in systems and population biology.

Tina Toni1, Michael P H Stumpf

  • 1Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW72AZ London, UK. ttoni@imperial.ac.uk

Bioinformatics (Oxford, England)
|November 3, 2009
PubMed
Summary
This summary is machine-generated.

We developed a new statistical framework for selecting the best mechanistic model in systems biology. This approach uses approximate Bayesian computation and sequential Monte Carlo sampling for complex biological systems with uncertain data.

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 19, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

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

Area of Science:

  • Computational Biology
  • Systems Biology
  • Biomedical Sciences

Background:

  • Computer simulations are vital in biomedical sciences for understanding complex biological systems.
  • Selecting the most accurate mechanistic model from multiple hypotheses is challenging, especially in systems biology.
  • Measurement uncertainty and limited assay capabilities complicate model selection in biological research.

Purpose of the Study:

  • To develop a robust statistical framework for rational model selection in systems biology.
  • To enable selection between different mechanistic models when exact likelihoods are intractable.
  • To provide a tool for choosing the best model that balances complexity and data fit.

Main Methods:

  • Developed a model selection framework using approximate Bayesian computation (ABC).
  • Employed sequential Monte Carlo (SMC) sampling within the ABC framework.
  • Applied the framework to diverse biological scenarios, including signal transduction and gene regulation.

Main Results:

  • The developed framework successfully selects between competing mechanistic models.
  • Demonstrated applicability across various biological systems, including influenza dynamics and the JAK-STAT signaling pathway.
  • Bayesian model selection effectively balances model complexity with the ability to describe observed data.

Conclusions:

  • The new framework provides a powerful tool for model selection in systems biology.
  • Enables the application of Bayesian model selection to any system that can be simulated, even without tractable likelihoods.
  • Facilitates rational decision-making between complex biological models using real-world data.