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 Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Types of Coprecipitation01:10

Types of Coprecipitation

Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...
Theories of Dissolution: Diffusion Layer Model01:15

Theories of Dissolution: Diffusion Layer Model

Dissolution, the process by which drug particles dissolve in a solvent, is explained by the diffusion layer model, a theoretical framework that simulates the absorption of oral drugs and allows us to analyze experimental data.
This process starts with a thin layer, saturated with the drug, forming at the interface between the solid and liquid. The solute then diffuses from this layer into the main solution. The Noyes-Whitney equation suggests that the rate of dissolution relies on the diffusion...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...

You might also read

Related Articles

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

Sort by
Same author

Molecular noise modulates transitions in the cell-fate differentiation landscape.

NPJ systems biology and applications·2026
Same author

Fifty years since a simple equation described the chaos of biology.

Nature·2026
Same author

Learning cell-specific networks from dynamics and geometry of single cells.

Cell systems·2025
Same author

Towards a mathematical framework for modelling cell fate dynamics.

Journal of mathematical biology·2025
Same author

The topological properties of the protein universe.

Nature communications·2025
Same author

Mapping, Modeling, and Reprogramming Cell-Fate Decision-Making Systems.

Annual review of biomedical data science·2025
Same journal

RNA-ligand complexes and the attenuation of neutral confinement in the evolution of RNA secondary structures.

Journal of the Royal Society, Interface·2026
Same journal

Individual detachment-reintegration events in homing pigeon flocks and the dominance of directional adjustment in their kinematic features.

Journal of the Royal Society, Interface·2026
Same journal

Thermal stress disrupts symbiotic fluid dynamics in bobtail squid.

Journal of the Royal Society, Interface·2026
Same journal

Distinct geometrical landscapes distinguish between modes of tristability in gene regulatory networks.

Journal of the Royal Society, Interface·2026
Same journal

Slow modulation of the contraction patterns in Physarum polycephalum.

Journal of the Royal Society, Interface·2026
Same journal

Moo-ving mountains: grazing agents drive terracette formation on steep hillslopes.

Journal of the Royal Society, Interface·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2026

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

Incomplete and noisy network data as a percolation process.

Michael P H Stumpf1, Carsten Wiuf

  • 1Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW72AZ, UK. m.stumpf@imperial.ac.uk

Journal of the Royal Society, Interface
|April 10, 2010
PubMed
Summary
This summary is machine-generated.

Network data noise and incompleteness can destroy the giant connected component (GCC). Our percolation analysis reveals how sampling affects GCC existence and sets bounds on false-positive rates, with implications for protein-protein interaction data.

More Related Videos

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

Related Experiment Videos

Last Updated: Jun 14, 2026

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

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography
08:02

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

Area of Science:

  • Network science
  • Graph theory
  • Statistical physics

Background:

  • The existence of a giant connected component (GCC) in random graphs is crucial for network analysis.
  • Understanding the impact of data imperfections on network structure is essential.
  • Percolation theory provides a framework for studying connectivity in random graphs.

Purpose of the Study:

  • To investigate the effects of noisy and incomplete network data on the existence of a GCC.
  • To derive percolation thresholds for random graphs with specified degree distributions.
  • To relate theoretical findings to experimental protein-protein interaction data.

Main Methods:

  • Analysis of random graphs with specified degree distributions.
  • Application of percolation theory to model network connectivity.
  • Derivation of percolation thresholds for GCC existence.
  • Investigation of sampling and noise effects on network structure.

Main Results:

  • Noisy and incomplete network data can significantly impact or destroy the GCC.
  • Both sampling and noise were found to disrupt the perceived existence of a GCC.
  • The absence of a GCC imposes a theoretical upper bound on the false-positive rate.
  • Percolation analysis was successfully related to experimental protein-protein interaction data.

Conclusions:

  • Data quality is a critical factor in determining the presence of a GCC in real-world networks.
  • Theoretical models of random graphs and percolation can provide insights into network data limitations.
  • The findings have implications for the interpretation of network analysis, particularly in fields like systems biology.