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

Network Function of a Circuit01:25

Network Function of a Circuit

408
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
408
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

334
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
334
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.0K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.0K
Block Diagram Reduction01:22

Block Diagram Reduction

300
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
300
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

104
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...
104
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

174
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...
174

You might also read

Related Articles

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

Sort by
Same author

Predictive modeling in biology and medicine: Digital twins and multi-scale modeling.

PLoS computational biology·2026
Same author

Women's health initiative strong and healthy silent atrial fibrillation recording study: Rationale, study design, and baseline data.

American heart journal·2026
Same author

Generation of two induced pluripotent stem cell lines from hypertrophic cardiomyopathy patients carrying MYBPC3 mutations.

Stem cell research·2026
Same author

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

NPJ systems biology and applications·2026
Same author

Dynamics of attractor transitions in Boolean networks under noise.

Chaos (Woodbury, N.Y.)·2026
Same author

Canalization as a stabilizing principle of gene regulatory networks: a discrete dynamical systems perspective.

NPJ systems biology and applications·2026
Same journal

Slow Evolution Towards Generalism in a Model of Variable Dietary Range.

Bulletin of mathematical biology·2026
Same journal

CBINN: Cancer Biology-Informed Neural Network for Unknown Parameter Estimation and Missing Physics Identification.

Bulletin of mathematical biology·2026
Same journal

A Cost-Sensitive Behavioral Modeling Analysis of the Early Identification and Control of Infectious Diseases.

Bulletin of mathematical biology·2026
Same journal

Tracking Dynamics of Superspreading Through Contacts, Exposures, and Transmissions in Edge-Based Network Epidemics.

Bulletin of mathematical biology·2026
Same journal

The Exact Hypergeometric Posterior Method for Accurate Inference of Population Size from Mark-Recapture Data.

Bulletin of mathematical biology·2026
Same journal

Modeling, Analysis, and Optimal Control of Leukemic Cell Population Dynamics Under Therapy.

Bulletin of mathematical biology·2026
See all related articles

Related Experiment Video

Updated: Sep 19, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K

Modular Control of Boolean Network Models.

David Murrugarra1, Alan Veliz-Cuba2, Elena Dimitrova3

  • 1Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA. murrugarra@uky.edu.

Bulletin of Mathematical Biology
|June 3, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new modular approach for controlling biological networks, simplifying complex models. It efficiently identifies essential control strategies and reduces computational challenges in network analysis.

Keywords:
Boolean networksCanalizationControlGene regulatory networksModularity

More Related Videos

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
Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

8.6K

Related Experiment Videos

Last Updated: Sep 19, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

11.8K
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
Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
10:32

Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits

Published on: April 15, 2015

8.6K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Science

Background:

  • Understanding control mechanisms in biological networks is essential for applications in biomedicine and metabolic engineering.
  • Boolean networks are computational models used to represent gene regulation, signaling, and metabolic pathways.
  • Previous work established a theoretical framework for modularity in Boolean networks, leading to a semidirect product decomposition.

Purpose of the Study:

  • To present a model-based control approach that leverages modular structure and canalizing features in Boolean networks.
  • To develop methods for identifying control strategies within individual network modules.
  • To establish a criterion for excluding non-contributory modules to network control.

Main Methods:

  • Exploiting modular network structure for control strategy identification.
  • Utilizing canalizing features of regulatory mechanisms to simplify network models.
  • Developing an efficient computational approach for identifying global control inputs in large networks.

Main Results:

  • A novel method for identifying control strategies based on modularity and canalizing features.
  • A criterion to identify and exclude modules that do not contribute to network control.
  • An efficient approach to solving computationally challenging control problems in moderately sized networks.

Conclusions:

  • The proposed modular approach offers an efficient solution for model-based control of biological networks.
  • This method simplifies the identification of minimal control sets for specific objectives.
  • Application to a T-LGL leukemia model demonstrates the practical utility of the approach.