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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

342
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...
342
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

320
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...
320
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

701
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...
701
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

467
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.
467
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

359
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
359
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

360
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...
360

You might also read

Related Articles

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

Sort by
Same author

Short-term Bayesian influenza forecasting in a hospital environment using a linear Kalman filter.

Journal of theoretical biology·2026
Same author

Infinitesimal homeostasis in mass-action systems.

Journal of mathematical biology·2026
Same author

Inferring structure and parameters of stochastic reaction networks with logistic regression.

PloS one·2026
Same author

Model fit vs. predictive reliability: a case study of the 1978 influenza outbreak.

Scientific reports·2025
Same author

How to correctly fit an SIR model to data from an SEIR model?

Mathematical biosciences·2024
Same author

Dynamic SARS-CoV-2 surveillance model combining seroprevalence and wastewater concentrations for post-vaccine disease burden estimates.

Communications medicine·2024
Same journal

Evaluation of aerodynamic characteristics of a coupled fluid-structure system using generalized Bernoulli's principle: An application to vocal folds vibration.

Journal of coupled systems and multiscale dynamics·2018
Same journal

Connecting within and between-hosts dynamics in the influenza infection-staged epidemiological models with behavior change.

Journal of coupled systems and multiscale dynamics·2017
Same journal

Predicting mechanism of biphasic growth factor action on tumor growth using a multi-species model with feedback control.

Journal of coupled systems and multiscale dynamics·2014
Same journal

Mathematical modeling approaches in the study of glaucoma disparities among people of African and European descents.

Journal of coupled systems and multiscale dynamics·2014
See all related articles

Related Experiment Video

Updated: Apr 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K

Algebraic Statistical Model for Biochemical Network Dynamics Inference.

Daniel F Linder1, Grzegorz A Rempala2

  • 1Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, P.O. Box 8015 Statesboro, GA 30460.

Journal of Coupled Systems and Multiscale Dynamics
|December 20, 2014
PubMed
Summary
This summary is machine-generated.

This study formalizes the stoichiometric algebraic statistical model (SASM) for analyzing complex biological networks. The enhanced SASM demonstrates sparsistency, accurately identifying molecular interactions in both simulated and real biological data.

Keywords:
Algebraic Statistical ModelBiochemical NetworksDNA- and RNA-based TechnologiesLaw of Mass ActionParameter InferenceSystems Biology

More Related Videos

The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

32.0K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K

Related Experiment Videos

Last Updated: Apr 19, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.7K
The Use of Chemostats in Microbial Systems Biology
13:19

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

32.0K
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.7K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Biology

Background:

  • Modern molecular quantification methods generate complex longitudinal data on RNA and DNA concentrations.
  • Inferring cellular-level interactions from such data is crucial for understanding biological systems.
  • The stoichiometric algebraic statistical model (SASM) offers a framework for analyzing conic (single source) networks but has been limited to heuristic studies.

Purpose of the Study:

  • To provide a formal mathematical treatment of the SASM.
  • To extend the SASM to analyze reaction systems decomposable into multiple conic subnetworks.
  • To demonstrate the sparsistency property of the extended SASM.

Main Methods:

  • Formal mathematical analysis of the stoichiometric algebraic statistical model (SASM).
  • Extension of the SASM to handle decomposable networks composed of multiple conic subnetworks.
  • In silico simulations and analysis of biological data from zebrafish retina experiments.

Main Results:

  • The extended SASM is mathematically validated for decomposable networks.
  • The sparsistency property is proven for the extended SASM, ensuring asymptotic discarding of false interactions.
  • The model successfully identified potential transcription factors for heat shock protein 70 (Hsp70) in zebrafish.

Conclusions:

  • The formalized and extended SASM provides a robust mathematical framework for network inference.
  • The sparsistency property ensures reliable identification of molecular interactions.
  • The SASM is applicable to both simulated and experimental biological data, offering insights into gene regulation.