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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial Gene Control

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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...
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Regulation of Expression at Multiple Steps01:23

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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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Constitutive and Regulated Gene Expression01:27

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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...
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Operon Model01:23

Operon Model

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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...
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Regulation of Expression Occurs at Multiple Steps02:24

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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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Translational Regulation

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Translational regulation in prokaryotes ensures efficient protein synthesis by controlling ribosome access to mRNA. This regulation is mediated by secondary RNA structures, including translational riboswitches, RNA thermometers, and small RNAs (sRNAs), which respond to intracellular and environmental signals to modulate gene expression.Translational RiboswitchesRiboswitches in the leader region of mRNAs can regulate translation by altering the accessibility of the Shine-Dalgarno (SD) sequence,...
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Updated: Sep 8, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A Computational Framework for Optimal and Model Predictive Control of Stochastic Gene Regulatory Networks.

Hamza Faquir, Manuel Pajaro, Irene Otero-Muras

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    Summary

    This study introduces a new computational framework for controlling gene regulatory networks, efficiently managing molecular noise for precise cell population engineering. The method optimizes control strategies for complex cellular behaviors and dynamic tracking in synthetic biology applications.

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    Area of Science:

    • Systems Biology
    • Synthetic Biology
    • Control Theory

    Background:

    • Designing controllers for cellular functions faces challenges in managing molecular noise.
    • Accurate and efficient solution of the Chemical Master Equation is a bottleneck for model-based control of stochastic biomolecular systems.

    Purpose of the Study:

    • Develop a framework for optimal and Model Predictive Control of stochastic gene regulatory networks.
    • Address limitations in computational efficiency and precise control over cell population behavior.
    • Provide robust handling of intrinsic molecular noise in biological systems.

    Main Methods:

    • Utilized an efficient approximation of the Chemical Master Equation via Partial Integro-Differential Equations.
    • Implemented an adjoint-based optimization method for enhanced control.
    • Applied the framework to stochastic gene regulatory networks.

    Main Results:

    • Achieved high computational efficiency in control system design.
    • Demonstrated precise control over the probability density function for complex cell population behaviors, including bimodality.
    • Showcased robust handling of intrinsic molecular noise.

    Conclusions:

    • The developed framework offers significant advantages for Cybergenetics and Synthetic Biology.
    • Enables fine-tuning of cell populations for emergent properties and dynamic tracking.
    • Provides an effective approach for model-based control of noisy biomolecular systems.