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Related Concept Videos

Protein Networks02:26

Protein Networks

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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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Biofilms are complex communities of microorganisms encased in a self-produced extracellular polysaccharide matrix attached to surfaces. These microbial consortia can include single or multiple species, providing enhanced survival benefits by forming organized, multilayered structures.The formation of biofilms occurs through four key stages: attachment, colonization, development, and dispersal.During attachment, free-swimming planktonic cells adhere to a surface, often facilitated by...
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The Significance of Membrane Transport01:44

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The transport of solutes across the cell membrane is essential for metabolic processes, like maintaining cell size and volume, generating the action potential, exchanging nutrients and gases, etc. Membrane transport can be either passive or active. It can be simple diffusion, facilitated, or mediated transport aided by transport proteins such as transporters and channels.
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Overview of Cell-Matrix Interactions01:24

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The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
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IP3/DAG Signaling Pathway01:11

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Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and...
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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...
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Information Fragmentation, Encryption and Information Flow in Complex Biological Networks.

Clifford Bohm1,2, Douglas Kirkpatrick2,3, Victoria Cao2,3

  • 1Department of Integrative Biology, Michigan State University, East Lansing, MI 48823, USA.

Entropy (Basel, Switzerland)
|May 28, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new information-theoretic tool to map information storage and processing in biological networks. This method reveals how information is fragmented and encrypted, offering insights into complex network functions.

Keywords:
computational complexityinformation fragmentationinformation processingneural network evolution

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Area of Science:

  • Neuroscience
  • Molecular Genetics
  • Computational Biology
  • Systems Biology

Background:

  • Understanding information storage in biological networks (neuronal, genetic) is crucial but current tools often focus on structure over function.
  • Existing methods lack the ability to precisely locate and quantify information within complex biological systems.

Purpose of the Study:

  • Introduce a novel information-theoretic tool, information fragmentation analysis, to assess information localization, fragmentation, and encryption in biological networks.
  • Develop methods to visualize information flow and quantify processing complexity within these networks.

Main Methods:

  • Information fragmentation analysis applied to full phenotypic data.
  • Creation of information fragmentation matrices and information flow graphs.
  • Analysis of information processing in evolved artificial brains (in silico).

Main Results:

  • Successfully localized information within complex networks and quantified its fragmentation and encryption.
  • Information fragmentation analysis provided deeper insights into information processing and cognitive functions in artificial brains.
  • Quantified differences in information processing complexity between early sensory data exposure and later routine processing.

Conclusions:

  • Information fragmentation analysis is a powerful tool for understanding information dynamics in biological and artificial networks.
  • The method offers a functional perspective, complementing structural analyses, to reveal how networks process and store information.
  • This approach quantifies information processing complexity and its variations across different processing stages.