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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

47
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...
47
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

168
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
168
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

109
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
109
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
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...
35
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

64
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.
64
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

66
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...
66

You might also read

Related Articles

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

Sort by
Same author

Does Timing Matter? Exploring the Effects of Measurement Error on Models.

Bulletin of mathematical biology·2026
Same author

A potential surrogate model for efficient inference of stochastic GLUT4 translocation.

Mathematical biosciences·2026
Same author

Does GLUT4 Queue? A Mechanistic Mathematical Model for Insulin Response in Adipocytes.

Bulletin of mathematical biology·2025
Same author

Oncolytic virus treatment of human breast cancer cells: Modelling therapy efficacy.

Journal of theoretical biology·2022
Same author

Emergence of a Neolithic in highland New Guinea by 5000 to 4000 years ago.

Science advances·2020
Same author

Longitudinal Changes in Insulin Resistance in Normal Weight, Overweight and Obese Individuals.

Journal of clinical medicine·2019
Same journal

Mathematical Modeling Shows that Overall Infection Burden is Reduced More by Vaccines that Decrease Spread or Accelerate Recovery than those that Lower Severe Infections or Death.

Bulletin of mathematical biology·2026
Same journal

Effects of Seasonal Births and Predation on Disease Spread.

Bulletin of mathematical biology·2026
Same journal

Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models.

Bulletin of mathematical biology·2026
Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026
Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026
Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K

The Distance Between: An Algorithmic Approach to Comparing Stochastic Models to Time-Series Data.

Brock D Sherlock1,2, Marko A A Boon2, Maria Vlasiou3

  • 1School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia.

Bulletin of Mathematical Biology
|July 26, 2024
PubMed
Summary
This summary is machine-generated.

This study identifies robust distance metrics for comparing stochastic models with experimental data. Integrated distance measures, like Wasserstein-1, are better for parameter inference and model validation than discrete measures, especially with noisy, time-evolving biological data.

Keywords:
Distance between evolving distributionsDistance metricsMultiple experimentsTime-series data

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Related Experiment Videos

Last Updated: Jun 19, 2025

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Macroscopic cellular processes are understood via mean-field models.
  • Stochastic models offer molecular-level insights but require quantitative validation.
  • Experimental biological data often feature small sample sizes and evolving distributions.

Purpose of the Study:

  • To identify suitable distance metrics for comparing stochastic model outputs with time-evolving experimental data.
  • To find metrics that facilitate parameter inference, model comparison, and model validation.
  • To address challenges posed by small sample sizes and temporal dynamics in biological data.

Main Methods:

  • Compared stochastic model outputs to synthetic data across multiple experimental scales.
  • Evaluated discrete (Kolmogorov-Smirnov) and integrated (Wasserstein-1) distance metrics based on empirical cumulative distribution functions (ECDFs).
  • Assessed metric sensitivity to parameter changes and robustness to added noise simulating experimental error.

Main Results:

  • Integrated distance metrics exhibited smoother parameter transitions compared to discrete measures.
  • Integrated metrics demonstrated robustness to noise, effectively replicating experimental error.
  • Discrete measures showed high sensitivity only near true parameters, limiting their utility.

Conclusions:

  • Integrated distance metrics are superior for fitting stochastic models to real-world biological data.
  • These findings enable the design of algorithms for accurate stochastic model parameterization.
  • The study provides a foundation for advancing molecular-scale understanding of cellular systems.