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

Cell Signaling Feedback Loops01:07

Cell Signaling Feedback Loops

Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
Negative feedback loops
Most signaling systems have negative feedback loops that can perform different functions such as output limiter, and adaptation.
Output limiter
Upon receiving an input signal, the cellular response rapidly increases until a threshold is reached. Beyond this threshold, a negative feedback loop...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
Network Function of a Circuit01:25

Network Function of a Circuit

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.
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
Operon Model01:23

Operon Model

The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...

You might also read

Related Articles

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

Sort by
Same author

Self-organized collapse of societies.

Physical review. E·2026
Same author

Epidemics with asymptomatic transmission: Subcritical phase from recursive contact tracing.

Physical review. E·2021
Same author

Self-organized criticality in neural networks from activity-based rewiring.

Physical review. E·2021
Same author

Discrimination emerging through spontaneous symmetry breaking in a spatial prisoner's dilemma model with multiple labels.

Physical review. E·2020
Same author

Repulsion in controversial debate drives public opinion into fifty-fifty stalemate.

Physical review. E·2019
Same author

Critical excitation-inhibition balance in dense neural networks.

Physical review. E·2019

Related Experiment Video

Updated: Jul 4, 2026

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression
11:23

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

Boolean network models of cellular regulation: prospects and limitations.

Stefan Bornholdt1

  • 1Institute for Theoretical Physics, University of Bremen, Bremen, Germany. bornholdt@itp.uni-bremen.de

Journal of the Royal Society, Interface
|May 30, 2008
PubMed
Summary

Boolean networks, simplified models of biological systems, surprisingly capture complex regulatory patterns. These models, even with noise, offer insights into cellular dynamics and network robustness.

More Related Videos

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Related Experiment Videos

Last Updated: Jul 4, 2026

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression
11:23

A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

Published on: October 6, 2019

Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Area of Science:

  • Systems biology
  • Computational biology
  • Network science

Background:

  • Understanding cellular regulatory machinery is crucial.
  • Traditional models range from detailed biochemical to coarse-grained network approaches.
  • Coarse-grained models simplify regulatory networks to nodes and links for architectural analysis.

Purpose of the Study:

  • To review discrete dynamical network models, specifically Boolean networks, for biological regulatory systems.
  • To explore Boolean networks extended with stochastic noise to study noise robustness.
  • To discuss the effectiveness of simple models in describing biological complexity.

Main Methods:

  • Graph-theoretical classification of regulatory circuits.
  • Development and analysis of discrete dynamical network models (Boolean networks).
  • Incorporation of stochastic noise into Boolean network models.

Main Results:

  • Boolean networks, despite their simplicity, can accurately match biological regulatory patterns.
  • Stochastic noise can be integrated to investigate network robustness.
  • These models provide a framework for understanding biological circuit dynamics.

Conclusions:

  • Simple Boolean models offer valuable insights into complex biological regulatory networks.
  • Network topology plays a role in noise robustness.
  • Boolean models are promising for exploratory analysis of biological circuits and mutants.