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

Propagation of Action Potentials01:23

Propagation of Action Potentials

10.8K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
10.8K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
Protein Networks02:26

Protein Networks

2.9K
No description available
2.9K
Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

18.9K
When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
18.9K
Network Function of a Circuit01:25

Network Function of a Circuit

974
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.
974
Intracellular Signaling Cascades01:24

Intracellular Signaling Cascades

54.2K
Once a ligand binds to a receptor, the signal is transmitted through the membrane and into the cytoplasm. The continuation of a signal in this manner is called signal transduction. Signal transduction only occurs with cell-surface receptors, which cannot interact with most components of the cell, such as DNA. Only internal receptors can interact directly with DNA in the nucleus to initiate protein synthesis. When a ligand binds to its receptor, conformational changes occur that affect the...
54.2K

You might also read

Related Articles

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

Sort by
Same author

Optimizing disorder with machine learning to harness phase synchronization.

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

Controlling severe atopic dermatitis dynamics.

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

Unsupervised Learning for Anticipating Critical Transitions.

Physical review letters·2026
Same author

Noncooperative Quantum Networks.

Physical review letters·2026
Same author

How heterogeneity shapes dynamics and computation in the brain.

Neuron·2025
Same author

Neuromorphic reservoir computing.

Chaos (Woodbury, N.Y.)·2025
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

2.4K

Cascade-based attacks on complex networks.

Adilson E Motter1, Ying-Cheng Lai

  • 1Department of Mathematics, Center for Systems Science and Engineering Research, Arizona State University, Tempe 85287, USA. motter@chaos3.la.asu.edu

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 7, 2003
PubMed
Summary
This summary is machine-generated.

Attacks on complex networks, like the internet and power grids, can cause widespread failures. Load redistribution in heterogeneous networks makes them vulnerable to cascading overloads from single node attacks.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Related Experiment Videos

Last Updated: Mar 8, 2026

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions
07:38

Author Spotlight: Modular Neuronal Networks for Analyzing Brain Functions

Published on: June 7, 2024

2.4K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.6K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Cybersecurity

Background:

  • Modern societies rely on large, interconnected networks (e.g., financial, communication, transportation).
  • The flow of physical quantities and node loads are critical operational parameters in these networks.
  • Real-world networks often exhibit highly heterogeneous load distributions.

Purpose of the Study:

  • To investigate the impact of intentional attacks on complex networks with redistributable loads.
  • To demonstrate the vulnerability of heterogeneous networks to cascading overload failures.
  • To highlight security concerns arising from network attack dynamics.

Main Methods:

  • Simulating intentional attacks on network models with load redistribution.
  • Analyzing the cascading effects of node failures.
  • Examining the role of load heterogeneity in network collapse.

Main Results:

  • Intentional attacks can trigger cascading overload failures in networks with redistributable loads.
  • Network heterogeneity significantly increases vulnerability, enabling large-scale collapse from single node attacks.
  • Attacks on key nodes can initiate system-wide failures.

Conclusions:

  • Complex networks with heterogeneous load distributions are inherently vulnerable to cascading failures initiated by targeted attacks.
  • The findings underscore critical security risks for essential infrastructure like the Internet and power grids.
  • Understanding these vulnerabilities is crucial for developing robust network defense strategies.