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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

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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,...
pV-Diagrams01:18

pV-Diagrams

The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
Combinatorial Gene Control02:33

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

Gene expression in prokaryotes is governed by constitutive and regulated systems, allowing cells to balance the production of essential proteins with adaptive responses to environmental changes.Constitutive Gene ExpressionConstitutive, or housekeeping, genes are continuously expressed as they encode proteins vital for fundamental cellular processes. These include enzymes for glycolysis, ribosomal components for protein synthesis, and proteins involved in DNA replication. Their constant...

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Updated: Jun 15, 2026

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

Statecharts for gene network modeling.

Yong-Jun Shin1, Mehrdad Nourani

  • 1Department of Electrical Engineering, University of Texas at Dallas, Richardson, Texas, United States of America. yshin@utdallas.edu

Plos One
|February 27, 2010
PubMed
Summary
This summary is machine-generated.

Statecharts offer a structured approach to modeling complex gene networks, overcoming limitations of traditional state diagrams. This method enhances the systematic representation of intricate biological systems.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Traditional state diagrams (stategraphs) struggle with the complexity of large systems due to their flat structure.
  • Modeling dynamic systems, such as gene networks, requires efficient and scalable methods.

Purpose of the Study:

  • To introduce statecharts as a novel and effective method for modeling complex gene networks.
  • To demonstrate the utility of statecharts' features (hierarchy, recursion, concurrency) in representing gene network dynamics.

Main Methods:

  • Statecharts were employed to model five key gene network motifs.
  • The study utilized nested hierarchy and recursion to represent complex interlocked feed-forward loop networks.

Main Results:

  • Statecharts provide a structured and efficient way to model complex gene networks.
  • The approach successfully modeled various gene network motifs, including intricate feed-forward loops.

Conclusions:

  • Statecharts offer a powerful alternative for modeling large and complex gene networks.
  • The inherent features of statecharts facilitate a systematic and efficient representation of biological system dynamics.