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

267
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...
267
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

339
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...
339
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

314
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.
314
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

237
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...
237
Synthetic Biology02:55

Synthetic Biology

5.5K
Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
5.5K
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

1.8K
Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
1.8K

You might also read

Related Articles

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

Sort by
Same author

A Multimodal Workflow for Spatial Metabolic Neighborhood Mapping in Neural Rosette Cultures.

bioRxiv : the preprint server for biology·2026
Same author

Early Epigenetic and Metabolic Responses to the Adipocyte Secretome Reveal Stress-Adaptive States in Triple-Negative Breast Cancer.

bioRxiv : the preprint server for biology·2026
Same author

Neural network approaches, including use of topological data analysis, enhance classification of human induced pluripotent stem cell colonies by treatment condition.

PLoS computational biology·2025
Same author

Personalizing computational models to construct medical digital twins.

Journal of the Royal Society, Interface·2025
Same author

Non-Invasive Quality Control of Organoid Cultures Using Mesofluidic CSTR Bioreactors and High-Content Imaging.

Advanced materials technologies·2025
Same author

Personalizing computational models to construct medical digital twins.

bioRxiv : the preprint server for biology·2024
Same journal

Recent advancements and limitations of intestinal organoids for clinical applications.

Progress in biomedical engineering (Bristol, England)·2026
Same journal

Tissue Engineering Strategies for Annulus Fibrosus Repair: A Scoping Review of Repair Methods, Animal Models, and Evaluation Techniques.

Progress in biomedical engineering (Bristol, England)·2026
Same journal

Lagrangian deformation tracking for strain imaging.

Progress in biomedical engineering (Bristol, England)·2026
Same journal

Novel aptamers targeting heparan sulfate for delivery of RNA therapeutics in Alzheimer's disease.

Progress in biomedical engineering (Bristol, England)·2026
Same journal

A perspective on neuromechanical biomarkers for neurorehabilitation: towards reliable assessment in research and clinical practice.

Progress in biomedical engineering (Bristol, England)·2026
Same journal

Assessing the relevance of biosignal-controlled robotic rehabilitation technologies: a systematic review.

Progress in biomedical engineering (Bristol, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 10, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K

Hybrid computational modeling methods for systems biology.

Daniel A Cruz1, Melissa L Kemp2

  • 1School of Mathematics, Georgia Institute of Technology, Atlanta, GA, United States of America.

Progress in Biomedical Engineering (Bristol, England)
|November 25, 2025
PubMed
Summary
This summary is machine-generated.

Hybrid systems biology models blend diverse mathematical approaches to analyze complex biological systems. This review highlights tools and applications that integrate different modeling formats for deeper biological insights.

Keywords:
computational modelingpredictionsimulationsystems biology

More Related Videos

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.5K
Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

5.4K

Related Experiment Videos

Last Updated: Jan 10, 2026

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.1K
In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

14.5K
Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments
07:46

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

5.4K

Area of Science:

  • Systems biology
  • Computational biology
  • Mathematical modeling

Background:

  • Traditional systems biology models range from mechanistic to abstract, but these distinctions are blurring.
  • Increasing computational power enables bridging diverse time and length scales in biological models.
  • High-throughput data acquisition necessitates leveraging available measurements for ill-defined biological mechanisms or network topologies.

Purpose of the Study:

  • To survey modeling tools that blend two or more mathematical forms for describing time-dependent processes in multivariate biological systems.
  • To explore recent innovations and applications of hybrid modeling methodologies.

Main Methods:

  • Review of hybrid modeling approaches combining different mathematical formalisms.
  • Categorization of hybrid models, including continuous/discrete, mechanistic/inference, and deterministic/stochastic types.

Main Results:

  • Hybrid modeling offers advantages for gaining biological systems-level insight by combining diverse model formats.
  • Innovations in hybrid modeling methodologies are expanding their applicability.

Conclusions:

  • Hybrid modeling is a powerful approach for tackling complex biological questions.
  • Combining different model formats enhances the ability to gain systems-level understanding in biology.