Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

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...

Pharmacokinetic Models: Overview

Pharmacokinetic Models: Overview

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 assumptions,...

Pharmacokinetic Models: Comparison and Selection Criterion

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.

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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 Analysis

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)...

Characteristics of Life

Characteristics of Life

Biology is a natural science that studies life and living organisms, including their structure, function, development, interactions, evolution, distribution, and taxonomy. The field's scope is extensive and divided into several specialized disciplines, such as anatomy, physiology, ethology, genetics, and many more. All living things share a few key traits, including cellular organization, heritable genetic material and the ability to adapt/evolve, metabolism to regulate energy needs, the...

You might also read

Related Articles

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

Sort by

Same author

Am80 (tamibarotene) and ATRA induce highly similar molecular responses and myeloid differentiation in non-APL AML, enhanced by LSD1/GCN5 inhibition and increased RARA expression.

BMC cancer·2026

Same author

Finding Pathways in Reaction Networks Guided by Energy Barriers Using Integer Linear Programing.

Molecular informatics·2026

Same author

A formal approach to the hierarchical structures of microbial communities with negative interactions.

Computational and structural biotechnology journal·2025

Same author

Author Correction: Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms.

NPJ systems biology and applications·2025

Same author

Finding Thermodynamically Favorable Pathways in Chemical Reaction Networks Using Flows in Hypergraphs and Mixed-Integer Linear Programming.

Journal of chemical information and modeling·2025

Same author

Revealing the hierarchical structure of microbial communities.

Scientific reports·2024

Search research articles

Related Experiment Video

Updated: May 11, 2026

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Structure, function, and behaviour of computational models in systems biology.

Christian Knüpfer¹, Clemens Beckstein, Peter Dittrich

¹Artificial Intelligence Group, University of Jena, Ernst-Abbe-Platz 2, Jena, Germany. christian.knuepfer@uni-jena.de

BMC Systems Biology

|June 1, 2013

Summary

This summary is machine-generated.

This study introduces the "meaning facets" framework to formally define the semantics of computational biological models. This approach enhances model clarity and computer-aided Systems Biology research.

More Related Videos

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

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

Published on: June 21, 2022

Related Experiment Videos

Last Updated: May 11, 2026

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

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

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

Published on: June 21, 2022

Area of Science:

Systems Biology
Computational Biology
Bioinformatics

Background:

Increasing complexity of computational bio-models necessitates formal semantic descriptions.
Current bio-models lack formal specification of their full meaning, relying on natural language.
Formal semantics are crucial for computer-aided bio-modelling.

Purpose of the Study:

To present the "meaning facets" conceptual framework for rigorously specifying bio-model semantics.
To address the dual interpretation (intrinsic and extrinsic) of bio-models.
To provide a foundation for computer support in bio-modelling.

Main Methods:

Developed the "meaning facets" framework to define bio-model semantics.
Applied the framework to two cell cycle models to demonstrate its utility.
Integrated existing approaches for computer representation of bio-models.

Main Results:

The "meaning facets" framework offers a systematic approach to bio-model semantics.
It structures information for building, using, and exchanging bio-models.
The framework supports both intrinsic (mathematical) and extrinsic (biological) interpretations from structure, function, and behavior perspectives.

Conclusions:

The "meaning facets" framework provides a systematic and in-depth approach to bio-model semantics.
It structures essential information for biologists and serves as a foundation for computer-aided bio-modelling.
This framework establishes a new methodology for Systems Biology and facilitates collaborative research.