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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.
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,...
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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...
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Protein-protein Interfaces02:04

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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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Reachability Analysis in Probabilistic Biological Networks.

Haitham Gabr, Andrei Todor, Alin Dobra

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    |September 11, 2015
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    This study introduces PReach, a novel method for calculating the probability of signal transmission in uncertain cellular networks. PReach significantly improves computational efficiency for understanding cell signaling pathways.

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    Area of Science:

    • Computational Biology
    • Systems Biology
    • Bioinformatics

    Background:

    • Cellular signaling involves transmitting signals from membrane receptors to internal reporters via protein interaction chains.
    • Understanding signal reachability is crucial for studying cellular responses to external stimuli.
    • Unreliable interaction data complicates accurate modeling of these signaling pathways.

    Purpose of the Study:

    • To develop a novel computational method, PReach (Probabilistic Reachability), for precisely calculating signal reachability probability in uncertain biological networks.
    • To address the computational complexity of determining signal transmission probability in unreliable signaling networks.

    Main Methods:

    • PReach models uncertain interactions using bi-variate polynomials, transforming the reachability problem into polynomial multiplication.
    • Introduces novel polynomial collapsing operators to associate polynomial terms with paths and cuts, reducing computational complexity.
    • The method focuses on computing the probability of a signal reaching from a set of receptors to a set of reporters.

    Main Results:

    • PReach demonstrates significantly improved time complexity compared to existing solutions for probabilistic reachability problems.
    • Experimental results show orders-of-magnitude reduction in running time on real biological datasets.
    • The method effectively handles uncertainty in protein interaction data for signaling pathway analysis.

    Conclusions:

    • PReach offers a computationally efficient and accurate approach for assessing signal propagation in uncertain cellular networks.
    • The method provides valuable insights into cellular responses by quantifying signal reachability.
    • Availability of data and software facilitates further research and application in bioinformatics.