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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,...
Combinatorial Gene Control02:33

Combinatorial Gene Control

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.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein.
What is Gene Expression?01:36

What is Gene Expression?

A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then processed and...

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Related Experiment Video

Updated: Jun 2, 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

Modelling gene and protein regulatory networks with answer set programming.

Timur Fayruzov1, Jeroen Janssen, Dirk Vermeir

  • 1Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 (S9), 9000 Ghent, Belgium. timur.fayruzov@ugent.be

International Journal of Data Mining and Bioinformatics
|May 6, 2011
PubMed
Summary

We present a novel Answer Set Programming (ASP) framework for modeling biological interaction networks. This approach effectively replicates Boolean network behavior, accurately predicting steady states for yeast cell cycle networks.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Modeling biological regulatory networks is crucial for understanding cellular processes.
  • Existing methods for network modeling face limitations and are an active area of research.
  • Developing flexible and efficient computational frameworks is essential for systems biology.

Purpose of the Study:

  • To introduce a novel Answer Set Programming (ASP)-based framework for modeling biological interaction networks.
  • To create a general ASP framework adaptable for various network modeling tasks with minimal effort.
  • To specifically adapt the framework to emulate Boolean network dynamics.

Main Methods:

  • Developed a general Answer Set Programming (ASP) framework to define network semantics.
  • Configured the ASP framework to mimic the behavior of Boolean networks.
  • Applied the tuned ASP framework to model the cell cycle networks of Budding Yeast and Fission Yeast.

Main Results:

  • The proposed ASP framework successfully models biological interaction networks.
  • The framework allows for flexible expansion and incorporation of additional features.
  • Modeled yeast cell cycle networks and achieved steady-state predictions consistent with Boolean network models.

Conclusions:

  • Answer Set Programming (ASP) offers a powerful and flexible approach for modeling biological regulatory networks.
  • The developed ASP framework provides an efficient method for simulating network behavior, including Boolean dynamics.
  • This approach facilitates accurate prediction of cellular states, such as steady states in cell cycle networks.