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Related Concept Videos

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.
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Updated: Jun 23, 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

Genetic network identification using convex programming.

A Julius1, M Zavlanos, S Boyd

  • 1University of Pennsylvania, Department of Electrical and Systems Engineering, USA. agung@seas.upenn.edu

IET Systems Biology
|May 20, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm to identify gene regulatory networks from experimental data. The method efficiently finds the smallest network structure, incorporating prior biological knowledge.

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Last Updated: Jun 23, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Systems Biology
  • Molecular Biology
  • Bioinformatics

Background:

  • Gene regulatory networks (GRNs) model gene interactions crucial for transcription and translation.
  • Gene expression levels are often measured via mRNA concentration in microarray experiments.
  • Identifying GRN structure from genetic perturbation data is a key challenge in systems biology.

Purpose of the Study:

  • To develop a novel algorithm for identifying the minimal gene regulatory network from genetic perturbation data.
  • To incorporate and respect prior qualitative biological knowledge (e.g., gene-gene effects, sign of interaction) into network identification.
  • To address the computationally hard problem of L(0) minimization using convex programming.

Main Methods:

  • Development of a novel algorithm for GRN identification.
  • Utilizing convex programming relaxation for L(0) minimization.
  • Incorporation of a priori biological knowledge regarding network structure.

Main Results:

  • The algorithm successfully identifies the smallest genetic network explaining experimental data.
  • Demonstrated ability to integrate qualitative biological knowledge into the network inference process.
  • Successful application to real biological networks (E. coli SOS pathway, Drosophila melanogaster segmentation polarity network) and an artificial network.

Conclusions:

  • The proposed method provides an efficient approach for inferring gene regulatory network structures.
  • The algorithm's ability to incorporate prior biological knowledge enhances the accuracy and biological relevance of identified networks.
  • This approach offers a valuable tool for systems biology research, particularly for large-scale network analysis.