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Related Concept Videos

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...
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.
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...
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...
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...
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...

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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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Predicting missing expression values in gene regulatory networks using a discrete logic modeling optimization guided

Isaac Crespo1, Abhimanyu Krishna, Antony Le Béchec

  • 1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4362 Esch-Belval and Life Sciences Research Unit, University of Luxembourg, L-1511 Luxembourg, Luxembourg.

Nucleic Acids Research
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Summary

This study introduces a novel method to predict missing gene expression data using gene regulatory networks. The approach refines networks based on experimental data, improving predictions for cellular phenotypes.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Genomics

Background:

  • High-throughput technologies generate vast amounts of biological data, including genome-wide transcription levels and protein abundance.
  • Experimental data are frequently noisy and incomplete, posing challenges for accurate data analysis, biological modeling, and predictive applications.

Purpose of the Study:

  • To develop a computational method for predicting missing gene expression values.
  • To infer gene expression data for genes crucial to stable cellular phenotypes.
  • To contextualize gene regulatory networks (GRNs) using experimental expression data.

Main Methods:

  • A literature-based GRN is iteratively pruned using experimental expression data to reflect specific biological conditions.
  • A Boolean model is employed to compute network steady states.
  • An evolutionary algorithm is utilized to sample network interactions and identify positive feedback loops within pruned networks.

Main Results:

  • The method successfully predicts missing gene expression values by integrating local network connectivity with global network stability properties.
  • Iterative network pruning contextualizes the GRN to specific experimental conditions, enhancing prediction accuracy.
  • The approach provides robust gene expression inference, even with incomplete datasets.

Conclusions:

  • The proposed method offers a robust approach to inferring missing gene expression data, particularly for genes involved in stable cellular phenotypes.
  • Contextualizing GRNs using experimental data and network stability improves the reliability of computational predictions.
  • This work advances the analysis of noisy and incomplete high-throughput biological data.