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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,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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.
Operon Model01:23

Operon Model

The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
Regulation of Metabolism01:19

Regulation of Metabolism

Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...

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Related Experiment Video

Updated: Jun 9, 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

Computational methods for analyzing dynamic regulatory networks.

Anthony Gitter1, Yong Lu, Ziv Bar-Joseph

  • 1Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA. agitter@cs.cmu.edu

Methods in Molecular Biology (Clifton, N.J.)
|September 10, 2010
PubMed
Summary
This summary is machine-generated.

Analyzing time series expression data reveals dynamic cellular networks. Methods for clustering, inferring causality, and reconstructing regulatory networks are discussed, highlighting the importance of temporal biological data.

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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Related Experiment Videos

Last Updated: Jun 9, 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

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

Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Cellular regulatory networks are dynamic, responding to stimuli.
  • Most current high-throughput methods capture static network properties.
  • Temporal information is crucial for understanding dynamic biological systems.

Purpose of the Study:

  • To review methods for analyzing time series expression data.
  • To discuss approaches for reconstructing dynamic regulatory networks.
  • To highlight the integration of temporal and static biological data.

Main Methods:

  • Clustering algorithms for identifying functionally related genes from time series data.
  • Lagged correlation and regression analysis for inferring causality and interactions.
  • Methods for combining time series expression data with static data.

Main Results:

  • Overview of clustering techniques for temporal gene expression data.
  • Discussion of causal inference and interaction detection using time-lagged analyses.
  • Presentation of strategies for dynamic regulatory network reconstruction.

Conclusions:

  • Time series expression data is a valuable source of temporal information for biological networks.
  • Integrating temporal and static data enables more accurate reconstruction of dynamic regulatory networks.
  • The analysis of temporal biological data is increasingly important with growing data availability.