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

100
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...
100
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

117
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
117
Feedback Inhibition00:46

Feedback Inhibition

54.2K
Biochemical reactions are occurring constantly in cells, converting starting substances to different products, usually with the help of enzymes that speed the reactions. Without enzymes, it would take far too long for most reactions to occur to be useful to the cell!
54.2K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

143
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
143
Feedback Loops01:01

Feedback Loops

58.2K
In most cases, excessive hormone production is prevented by negative feedback—a loop that starts with a stimulus inducing the release of a particular substance, like a hormone, to maintain a certain level before triggering a signal that results in a decrease in further release of the hormone.
58.2K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

673
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
673

You might also read

Related Articles

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

Sort by
Same author

A Neyman-Pearson Framework for Modeling Cellular Decision Making Using Single-Cell TNF-NF-κB Signaling Data.

bioRxiv : the preprint server for biology·2026
Same author

Single-Cell Measurements and Modeling and Computation of Decision-Making Errors in a Molecular Signaling System with Two Output Molecules.

Biology·2023
Same author

Data-driven structural analysis of small cell lung cancer transcription factor network suggests potential subtype regulators and transition pathways.

NPJ systems biology and applications·2023
Same author

Hyperpolarized <sup>13</sup>C metabolic imaging detects long-lasting metabolic alterations following mild repetitive traumatic brain injury.

Research square·2023
Same author

Exploring extreme signaling failures in intracellular molecular networks.

Computers in biology and medicine·2022
Same author

Modeling and measurement of signaling outcomes affecting decision making in noisy intracellular networks using machine learning methods.

Integrative biology : quantitative biosciences from nano to macro·2020

Related Experiment Video

Updated: Aug 28, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

Learning feedback molecular network models using integer linear programming.

Mustafa Ozen1, Effat S Emamian2, Ali Abdi3

  • 1Department of Biochemistry, Vanderbilt University, Nashville, TN 37205, United States of America.

Physical Biology
|September 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel network learning approach using integer linear programming to create accurate computational models of molecular networks. The method improves predictions from experimental data, aiding disease understanding and drug development.

Keywords:
feedbacksinteger linear programminglearning network modelsmachine learningmolecular networksnetwork modeling

More Related Videos

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

2.3K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K

Related Experiment Videos

Last Updated: Aug 28, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K
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

2.3K
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

2.6K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Molecular network analysis is crucial for understanding complex diseases and identifying drug targets.
  • Existing computational models often fail to accurately predict experimental data due to incomplete or conflicting information.
  • Accurate network models are essential for biological discovery and therapeutic development.

Purpose of the Study:

  • To develop a network learning approach that systematically incorporates biological dynamics and regulatory mechanisms.
  • To present a method for effectively considering feedback paths during network learning.
  • To improve the accuracy of computational models for molecular networks.

Main Methods:

  • Integer linear programming formulation for network learning.
  • Incorporation of biological dynamics and regulatory mechanisms.
  • Method for considering feedback paths in network learning.

Main Results:

  • The proposed approach systematically learns molecular networks, incorporating biological dynamics.
  • A method to properly consider feedback paths during network learning was developed.
  • Application to the ERBB signaling network demonstrated the framework's utility.
  • The learning approach reduces the gap between curated networks and experimental data.

Conclusions:

  • The developed network learning methods enhance the reliability of computational models.
  • Calibrated networks lead to more biologically meaningful predictions.
  • This approach aids in understanding complex diseases and developing targeted therapies.