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Related Concept Videos

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.
Circuit Terminology01:14

Circuit Terminology

An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
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.
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Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

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Related Experiment Video

Updated: May 21, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

How networks change with time.

Yongjin Park1, Joel S Bader

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.

Bioinformatics (Oxford, England)
|June 13, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces new computational methods to track dynamic changes in protein complexes within cells. These tools help understand how cells adapt to environmental and genetic signals by analyzing gene expression and protein interactions.

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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Related Experiment Videos

Last Updated: May 21, 2026

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

Area of Science:

  • Systems Biology
  • Computational Biology
  • Molecular Cell Biology

Background:

  • Biological networks dynamically respond to genetic and environmental stimuli.
  • Cellular state changes are reflected in biomolecule abundance and protein complex composition.
  • Understanding dynamic cellular states is crucial for biological research.

Purpose of the Study:

  • To develop novel computational methods for inferring the dynamic state of protein complexes in cells.
  • To provide tools for analyzing how cellular components change over time.

Main Methods:

  • Protein expression inferred from transcription profile time courses.
  • Protein complexes identified through joint analysis of protein co-expression and protein-protein interaction networks.
  • Introduced Dynamical Hierarchical Agglomerative Clustering (DHAC) for time-evolving networks.
  • Developed MATCH-EM for matching clusters across time points.

Main Results:

  • DHAC demonstrates significant improvement over existing clustering methods using link prediction as a metric.
  • Applied methods to the yeast metabolic cycle, revealing correspondence between gene expression waves and protein complexes.
  • Identified regulatory mechanisms for mitochondrial ribosome assembly.
  • Illustrated dynamic changes in nuclear pore complex components.

Conclusions:

  • The developed methods effectively predict the dynamic state of protein complexes.
  • These tools offer new insights into cellular regulation and dynamic processes.
  • The findings advance the understanding of how cells adapt and function over time.