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: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

438
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...
438
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

305
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...
305
Causality in Epidemiology01:21

Causality in Epidemiology

1.8K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.8K
Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

1.4K
The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
1.4K
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

424
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.
424
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

370
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...
370

You might also read

Related Articles

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

Sort by
Same author

Single-cell profiling reveals that dynamic lung immune responses distinguish protection from susceptibility to tuberculosis.

PLoS pathogens·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026
Same author

Variogram Modeling of Spatially Variant Early Response to Therapy in Advanced Non-Small Cell Lung Cancer.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same author

KGBN: Augmenting and optimizing logical gene regulatory networks using knowledge graphs.

bioRxiv : the preprint server for biology·2026
Same author

Multiparametric MRI Markers Associated with Breast Cancer Risk in Women with Dense Breasts.

Cancers·2025
Same author

Verification and reproducible curation of the BioModels repository.

PLoS computational biology·2025

Related Experiment Video

Updated: Feb 24, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

716

Qualitative causal analyses of biosimulation models.

Maxwell L Neal1, John H Gennari1, Daniel L Cook1,2

  • 1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA.

CEUR Workshop Proceedings
|August 15, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for analyzing biosimulation models, enabling researchers to understand how changes affect the entire system. The approach uses formal ontologies and automated reasoning for qualitative, systems-level causal analysis.

Keywords:
automated inferencebiological modelingbiosimulationnetwork analysis

More Related Videos

In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.4K
Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design
06:18

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

Published on: December 3, 2020

4.4K

Related Experiment Videos

Last Updated: Feb 24, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

716
In Vivo Modeling of the Morbid Human Genome using Danio rerio
12:31

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

21.4K
Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design
06:18

Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

Published on: December 3, 2020

4.4K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Biosimulation models are crucial for understanding complex biological systems.
  • Current methods for analyzing these models lack efficient systems-level causal inference capabilities.
  • Interpreting model semantics, especially dependencies, is key for robust analysis.

Purpose of the Study:

  • To develop an approach for qualitative, systems-level causal analysis of biosimulation models.
  • To enable rapid investigation of how perturbations propagate through a modeled system.
  • To facilitate model development, testing, and application by providing network dynamics insights.

Main Methods:

  • Leveraging semantics-based modeling formats and formal ontology.
  • Annotating model semantics, focusing on physical properties and dependencies.
  • Augmenting the Ontology of Physics for Biology with OWL axioms and SWRL rules.
  • Utilizing automated reasoning for inferring qualitative causal relationships.

Main Results:

  • Demonstrated a method for qualitative, systems-level causal analysis of biosimulation models.
  • Showcased how a reasoner can infer the influence of annotated physical properties.
  • Enabled users to quickly investigate the propagation of qualitative perturbations within a model.

Conclusions:

  • The developed approach enhances the understanding of systems-level network dynamics in biosimulation models.
  • Formalizing model semantics and dependencies is critical for effective causal analysis.
  • This tool aids researchers in developing, testing, and applying biosimulation models more effectively.