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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,...
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Two-Compartment Open Model: Overview01:05

Two-Compartment Open Model: Overview

Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
The...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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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

Modeling information flow in biological networks.

Yoo-Ah Kim1, Jozef H Przytycki, Stefan Wuchty

  • 1National Center for Biotechnology Information, NLM, NIH, 8600 Rockville Pike, Bethesda, MD 20894, USA. kimy3@ncbi.nlm.nih.gov

Physical Biology
|May 17, 2011
PubMed
Summary
This summary is machine-generated.

This study models cellular communication in large molecular networks using information flow. The new computational method helps discover disease pathways, like those in glioma, by analyzing genotype-phenotype relationships.

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Area of Science:

  • Systems Biology
  • Network Science
  • Computational Biology

Background:

  • Large-scale molecular interaction networks offer a systems-level view of cellular processes.
  • Modeling network communications as information flow aids in dissecting dynamic dependencies between components.
  • Information flow models assume nodes communicate by propagating signals through intermediates.

Purpose of the Study:

  • To provide an overview of network analysis using information flow models.
  • To describe a novel computational method for discovering dysregulated pathways in glioma.
  • To generalize existing approaches for computing information flows in large networks and provide formal proofs.

Main Methods:

  • Overview of state-of-the-art network analysis based on information flow models.
  • Description of a generalized computational method for inferring information flow from genotype to phenotype.
  • Formal proof for the generalized information flow computation method.

Main Results:

  • The paper presents a comprehensive review of information flow models in network analysis.
  • A novel computational method is detailed for identifying dysregulated pathways in glioma.
  • The method is generalized to handle large numbers of instances in human interaction networks.

Conclusions:

  • Information flow modeling is a powerful approach for understanding cellular communication and network dynamics.
  • The developed computational method advances the discovery of disease-related pathways.
  • This work contributes to inferring complex biological relationships from large-scale network data.