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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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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Using SCOPE to Identify Potential Regulatory Motifs in Coregulated Genes
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Published on: May 31, 2011

Reverse engineering sparse gene regulatory networks using cubature kalman filter and compressed sensing.

Amina Noor1, Erchin Serpedin, Mohamed Nounou

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843-3128, USA.

Advances in Bioinformatics
|June 6, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new algorithm for gene regulatory network inference using cubature Kalman filter (CKF) and Kalman filter (KF) with compressed sensing. It accurately infers gene interactions from expression data.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Inferring gene regulatory networks (GRNs) is crucial for understanding cellular mechanisms.
  • Existing methods often struggle with the complexity and noise inherent in gene expression data.
  • State-space models offer a powerful framework for dynamic system analysis.

Purpose of the Study:

  • To develop a novel algorithm for accurate and robust inference of gene regulatory networks.
  • To leverage advanced filtering techniques and compressed sensing for improved parameter estimation.
  • To provide insights into regulatory relationships among genes within a biological system.

Main Methods:

  • Utilizing cubature Kalman filter (CKF) for hidden state estimation in a nonlinear gene expression model.
  • Employing compressed sensing-based Kalman filter (KF) for estimating system parameters modeled as a Gauss-Markov process.
  • Calculating the Cramér-Rao lower bound (CRLB) to benchmark parameter estimation accuracy.

Main Results:

  • The proposed algorithm demonstrates superior performance in accuracy and robustness across various synthetic data scenarios.
  • Effective inference of gene regulatory relationships was achieved using both in silico (DREAM4) and in vivo (IRMA) datasets.
  • The algorithm shows significant scalability with increasing numbers of genes and sample points.

Conclusions:

  • The combined CKF, KF, and compressed sensing approach provides a powerful tool for gene regulatory network inference.
  • The method offers a robust and accurate solution for analyzing complex biological systems.
  • This algorithm advances the field of systems biology by enabling more reliable network reconstruction.