Jove
Visualize
Contact Us

Related Concept Videos

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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...

You might also read

Related Articles

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

Sort by
Same author

Enhancing generalizability of model discovery across parameter space with multi-experiment equation learning for biological systems.

PLoS computational biology·2026
Same author

SSRCA: A Novel Machine Learning Pipeline to Perform Sensitivity Analysis for Agent-Based Models.

Bulletin of mathematical biology·2026
Same author

Mathematical modeling of multicellular tumor spheroids quantifies inter-patient and intra-tumor heterogeneity.

NPJ systems biology and applications·2025
Same author

Statistical and topological summaries aid disease detection for segmented retinal vascular images.

Microcirculation (New York, N.Y. : 1994)·2023
Same author

Topological data analysis distinguishes parameter regimes in the Anderson-Chaplain model of angiogenesis.

PLoS computational biology·2021
Same author

The influence of numerical error on parameter estimation and uncertainty quantification for advective PDE models.

Inverse problems·2021
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
Same journal

Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.

Bulletin of mathematical biology·2026
Same journal

The Exact Hypergeometric Posterior Method for Accurate Inference of Population Size from Mark-Recapture Data.

Bulletin of mathematical biology·2026
Same journal

Modeling, Analysis, and Optimal Control of Leukemic Cell Population Dynamics Under Therapy.

Bulletin of mathematical biology·2026
See all related articles
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 Experiment Video

Updated: Jun 21, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

Forecasting and Predicting Stochastic Agent-Based Model Data with Biologically-Informed Neural Networks.

John T Nardini1

  • 1Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, 08628, USA. nardinij@tcnj.edu.

Bulletin of Mathematical Biology
|September 22, 2024
PubMed
Summary
This summary is machine-generated.

Biologically-informed neural networks (BINNs) create interpretable differential equation (DE) models to accurately predict agent-based model (ABM) collective migration. This approach forecasts new data and explores uncharted parameter spaces efficiently.

Keywords:
Agent-based modelingData-driven modelingDifferential equationsMachine learning

More Related Videos

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

8.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

981

Related Experiment Videos

Last Updated: Jun 21, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

8.8K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

981

Area of Science:

  • Computational Biology
  • Mathematical Modeling
  • Artificial Intelligence

Background:

  • Collective cell migration is crucial for biological processes like wound healing and development.
  • Agent-based models (ABMs) simulate collective migration but are computationally intensive and difficult to parameterize.
  • Mean-field differential equation (DE) models offer faster simulations but can inaccurately represent ABM behavior in certain parameter spaces.

Purpose of the Study:

  • To develop a method using biologically-informed neural networks (BINNs) to create accurate and interpretable DE models for collective migration.
  • To enable the prediction of ABM behavior in both unseen data and unexplored parameter regions.
  • To enhance the efficiency of parameter space exploration for ABMs.

Main Methods:

  • Training biologically-informed neural networks (BINNs) to learn interpretable differential equation (DE) models from ABM data.
  • Utilizing BINN-guided partial differential equation (PDE) simulations for forecasting future ABM data.
  • Combining BINN-guided PDE simulations with multivariate interpolation to predict ABM behavior at new parameter values.
  • Validating the approach with three distinct ABMs of collective migration.

Main Results:

  • BINN-guided PDE simulations accurately forecast spatial ABM data not encountered during training.
  • The method successfully predicts ABM behavior in previously unexplored parameter ranges.
  • A single-compartment BINN-guided PDE accurately captured ABM dynamics where traditional mean-field models were ill-posed or required multiple compartments.
  • The approach demonstrated efficiency in exploring parameter space.

Conclusions:

  • Biologically-informed neural networks (BINNs) provide a powerful framework for developing accurate and interpretable DE models from ABMs.
  • This BINN-guided PDE approach significantly enhances the ability to forecast and predict collective migration dynamics across parameter spaces.
  • The methodology facilitates efficient exploration of ABM parameter spaces, opening avenues for data-driven tasks like parameter estimation from experimental data.