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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

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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...
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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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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
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Updated: Mar 6, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Learning a Probabilistic Boolean Network model from biological pathways and time-series expression data.

Vardaan Pahuja, Ritwik Kumar Layek, Pabitra Mitra

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    |March 9, 2017
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    Summary
    This summary is machine-generated.

    This study introduces a new algorithm for building accurate gene regulatory network models using biological pathways and gene expression data. The method constructs a Probabilistic Boolean Network (PBN) for precise modeling of gene dynamics.

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    Area of Science:

    • Systems Biology
    • Computational Biology
    • Bioinformatics

    Background:

    • Gene regulatory networks (GRNs) control gene expression.
    • Inferring GRNs is crucial for understanding cellular processes.
    • Existing methods may not fully integrate diverse biological data.

    Purpose of the Study:

    • To develop a novel algorithm for inferring stochastic models of gene regulatory networks.
    • To precisely model gene dynamics using both biological pathways and time-series expression data.

    Main Methods:

    • Proposed a new algorithm to construct a Probabilistic Boolean Network (PBN).
    • Utilized biological pathway information to build a pathway tree.
    • Employed time-series expression data to select an optimal tree level for PBN inference.

    Main Results:

    • The developed algorithm accurately models observed gene dynamics.
    • The PBN constructed provides a high degree of precision in gene expression modeling.
    • Integration of pathway and time-series data enhances model accuracy.

    Conclusions:

    • The novel algorithm effectively infers stochastic gene regulatory network models.
    • Probabilistic Boolean Networks are a powerful tool for systems biology research.
    • This approach offers a precise method for analyzing gene expression data.