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

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,...
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,...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Structural Protein Function01:56

Structural Protein Function

Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to form...
Structural Protein Function01:56

Structural Protein Function

Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to form...
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.

You might also read

Related Articles

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

Sort by
Same author

Optimal Multi-Drug Therapies for Antimicrobial Resistance with Horizontal Transfer.

Journal of optimization theory and applications·2026
Same author

Programmable artificial RNA condensates in mammalian cells.

Nature nanotechnology·2026
Same author

Molecular recruitment and release using DNA host condensates.

Nanoscale horizons·2026
Same author

Programmable artificial RNA condensates in mammalian cells.

bioRxiv : the preprint server for biology·2026
Same author

Internal Phase Separation in Synthetic DNA Condensates.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2025
Same author

Design of an intracellular aptamer-based fluorescent biosensor to track burden in Escherichia coli.

Trends in biotechnology·2025
Same journal

Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice.

BMC systems biology·2019
Same journal

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.

BMC systems biology·2019
Same journal

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.

BMC systems biology·2019
Same journal

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

BMC systems biology·2019
Same journal

GNE: a deep learning framework for gene network inference by aggregating biological information.

BMC systems biology·2019
Same journal

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

BMC systems biology·2019
See all related articles

Related Experiment Video

Updated: Jun 1, 2026

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

Structurally robust biological networks.

Franco Blanchini1, Elisa Franco

  • 1Dipartimento di Matematica ed Informatica, Universitá degli Studi di Udine, Via delle Scienze 206, 33100 Udine, Italy.

BMC Systems Biology
|May 19, 2011
PubMed
Summary
This summary is machine-generated.

Biological networks exhibit robust stability due to their inherent structure, not just parameter values. This study introduces a mathematical framework using control theory to analytically prove this structural robustness in biomolecular systems.

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

Related Experiment Videos

Last Updated: Jun 1, 2026

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

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

Area of Science:

  • Systems Biology
  • Theoretical Biology
  • Biophysics

Background:

  • Biological systems exhibit remarkable robustness in their regulatory functions despite component variability.
  • Robustness is often a structural property of biological networks, but systematic mathematical modeling is limited.
  • Numerical simulations are frequently used to study structural robustness, with few analytical methods available.

Purpose of the Study:

  • To develop a framework for analyzing the robust stability of equilibria in biological networks.
  • To provide analytical methods for characterizing structural robustness without extensive numerical simulations.
  • To demonstrate that network structure and interaction properties confer robustness independent of parameter values.

Main Methods:

  • Employing Lyapunov and invariant sets theory.
  • Focusing on the structural analysis of ordinary differential equation models.
  • Applying classical control theory principles to biomolecular networks.

Main Results:

  • Developed a framework for rigorous mathematical analysis of robust stability in biological networks.
  • Provided analytical proofs of robust stability for known biomolecular networks.
  • Demonstrated that structural properties, not just parameter values, ensure network robustness.

Conclusions:

  • Classical control theory offers powerful analytical tools for understanding biological network behavior.
  • The structure and qualitative interaction properties of biological networks are key drivers of their robustness.
  • Analytical methods can rigorously confirm robustness, complementing numerical approaches.