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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.
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...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Operon Model01:23

Operon Model

The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Related Experiment Video

Updated: May 19, 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

An integer optimization algorithm for robust identification of non-linear gene regulatory networks.

Nishanth Chemmangattuvalappil1, Keith Task, Ipsita Banerjee

  • 1Department of Chemical Engineering, University of Pittsburgh, 1249 Benedum Hall, 3700 O'Hara Street, Pittsburgh, PA 15261, USA.

BMC Systems Biology
|September 4, 2012
PubMed
Summary

This study introduces a novel algorithm for robustly identifying gene regulatory networks from noisy, non-linear gene expression data. The method integrates bootstrapping and integer programming to accurately predict network topology and interaction strength, even with limited experimental replicates.

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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
07:55

Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes

Published on: May 31, 2011

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Understanding cellular decision-making requires reverse engineering gene networks and identifying regulatory interactions.
  • High-throughput techniques generate vast gene expression data, but biological noise necessitates numerous experimental replicates for reliable analysis.
  • Existing algorithms often lack robustness and efficiency in handling noisy, non-linear biological data.

Purpose of the Study:

  • To develop a robust network identification algorithm for inferring gene regulatory network topology and interaction strength from time-series gene expression data.
  • To address challenges of experimental noise and non-linear dynamics inherent in biological systems.
  • To create an efficient and accurate method for predicting gene regulatory interactions using limited data.

Main Methods:

  • Developed a network identification algorithm formulated as an integer-programming problem, exploiting the sparsity of biological interactions.
  • Integrated bootstrap resampling with network identification to enhance robustness against data variability and noise.
  • Incorporated non-linearity in gene dynamics using the S-system formulation, optimizing for agreement between experimental and predicted gene dynamics.

Main Results:

  • The algorithm accurately infers both the topology and strength of regulatory interactions from noisy, non-linear time-series gene expression data.
  • Validation using in silico and experimental datasets demonstrated high precision and recall in predicting network topology and accurate kinetic parameters.
  • Predicted gene expression profiles closely matched the dynamics of the input data in both in silico and experimental case studies.

Conclusions:

  • The integer programming algorithm effectively identifies robust gene regulatory networks from noisy, non-linear time-series data by utilizing bootstrapping and exploiting network sparsity.
  • The developed method is highly relevant for analyzing biological systems, which are inherently noisy and non-linear.
  • The algorithm's validation against in silico and E. coli data suggests broad applicability to diverse biological systems.