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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,...
Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Overview of Cell Signaling01:23

Overview of Cell Signaling

Despite the protective membrane that separates a cell from the environment, cells need the ability to detect and respond to environmental changes. Additionally, cells often need to communicate with one another. Unicellular and multicellular organisms use a variety of cell signaling mechanisms to communicate with the environment.
Cells respond to many types of information, often through receptor proteins positioned on the membrane. For example, skin cells respond to and transmit touch...

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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

Predictive models for cellular signaling networks.

Dagmar Iber1, Georgios Fengos

  • 1Department of Biosystems, Science, and Engineering (D-BSSE), ETH Zurich, Basel, Switzerland. dagmar.iber@bsse.ethz.ch

Methods in Molecular Biology (Clifton, N.J.)
|January 31, 2013
PubMed
Summary
This summary is machine-generated.

This chapter introduces differential-equation models for biological networks, covering reaction kinetics and analysis techniques like phase plane analysis and bifurcation diagrams to understand system dynamics and parameter dependencies.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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

Background:

  • Biological regulatory networks are complex systems.
  • Understanding their dynamics is crucial for biological insights.
  • Mathematical modeling offers a powerful approach to study these networks.

Purpose of the Study:

  • To introduce the formulation and analysis of differential-equation-based models for biological regulatory networks.
  • To explain basic reaction types, mass action kinetics, and simplifying approximations for model development.
  • To present methods for analyzing dynamic systems, including phase plane analysis, linear stability, and bifurcation diagrams.

Main Methods:

  • Formulation of differential-equation models based on reaction types and mass action kinetics.
  • Application of simplifying approximations for biological signaling models.
  • Phase plane and linear stability analysis to determine system time evolution and long-term behavior.
  • Bifurcation diagrams to assess parameter dependency and network behaviors like oscillations and switches.
  • Calculation of sensitivity and robustness measures for signaling output.

Main Results:

  • Models for biological signaling can be developed using basic reaction types and kinetics.
  • Phase plane and stability analysis reveal system dynamics and attractors.
  • Bifurcation analysis identifies critical parameter values influencing network behavior.
  • Quantification of signaling output sensitivity and robustness is achievable.

Conclusions:

  • Differential-equation models provide a framework for understanding biological regulatory networks.
  • Analysis techniques enable the prediction of network dynamics, stability, and parameter-dependent behaviors.
  • Model-based analysis is essential for characterizing the robustness and sensitivity of biological signaling pathways.