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Related Concept Videos

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Related Experiment Video

Updated: Jun 17, 2026

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

Dynamic networks from hierarchical bayesian graph clustering.

Yongjin Park1, Cristopher Moore, Joel S Bader

  • 1Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA.

Plos One
|January 20, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a new dynamical model to analyze changing biological networks over time and space. It reveals how protein interactions evolve, offering deeper insights than static network analysis.

Related Experiment Videos

Last Updated: Jun 17, 2026

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
  • Computational Biology
  • Bioinformatics

Background:

  • Biological networks are dynamic, with protein synthesis and degradation causing constant change.
  • Understanding these temporal and spatial network dynamics is crucial for biological insight.
  • Current methods often simplify evolving networks into static snapshots, losing critical information.

Purpose of the Study:

  • To develop a novel approach for analyzing dynamic biological networks that accounts for time and tissue dependence.
  • To move beyond static network analysis by capturing the evolving nature of protein interactions.
  • To infer the spatiotemporal evolution of protein components in multicellular organisms.

Main Methods:

  • Introduced a dynamical hierarchical stochastic block model (HSBM) to group proteins by shared, evolving interaction patterns.
  • Allowed protein membership in blocks to change over time and space, reflecting cellular organization.
  • Inferred spatiotemporal protein evolution from transcript profiles, using Arabidopsis root development as a model system.

Main Results:

  • The dynamical HSBM model effectively analyzes evolving biological networks without extensive parameter tuning.
  • Outperformed existing snapshot-based methods in capturing network dynamics.
  • Identified specific protein modules associated with distinct cell types and developmental stages in Arabidopsis.

Conclusions:

  • The developed model provides a powerful tool for understanding dynamic biological networks in space and time.
  • This approach offers a more comprehensive view of biological systems compared to static analyses.
  • The model has potential applications beyond biology, including the analysis of dynamic social networks.