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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,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
Epistasis Analysis01:09

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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...

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Related Experiment Video

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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
08:38

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Published on: March 3, 2015

Beyond element-wise interactions: identifying complex interactions in biological processes.

Christophe Ladroue1, Shuixia Guo, Keith Kendrick

  • 1Department of Computer Science and Mathematics, Warwick University, Coventry, United Kingdom.

Plos One
|September 24, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces complex Granger causality to identify cooperative and competitive biological interactions from time-series data. The method reveals novel signal relationships, enhancing systems biology approaches.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Biological processes involve complex interactions between elements like genes and cells, often modeled as networks.
  • Traditional network models may not fully capture cooperative, competitive, or modulatory interactions.
  • Complex interactions, such as enzyme-mediated reactions, are crucial for biological functions.

Purpose of the Study:

  • To develop a method for identifying complex interactions in biological systems.
  • To extend Granger Causality to analyze multi-elemental group interactions.
  • To reveal novel relationships within biological time-series data.

Main Methods:

  • Utilized Granger Causality, a measure of signal interaction, to analyze influence.
  • Extended Granger Causality to multi-dimensional signals for group interaction analysis.
  • Applied the method to simulated data and biological datasets (yeast gene expression, brain activity, metabolic reactions).

Main Results:

  • Demonstrated that complex Granger causality can uncover new types of signal relationships.
  • The approach is particularly effective for analyzing biological data.
  • Identified complex causal interactions in yeast, brain, and metabolic data.

Conclusions:

  • Complex Granger causality offers a powerful tool for understanding biological systems.
  • The method reveals previously unidentified interaction patterns.
  • Identifying all complex causalities presents a computational challenge (NP-hard problem).