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Related Concept Videos

Constitutive and Regulated Gene Expression01:27

Constitutive and Regulated Gene Expression

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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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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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Operon Model01:23

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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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Positive and negative feedback loops are crucial for regulating biological signaling systems. These feedback loops are processes that connect output signals to their inputs.
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Related Experiment Video

Updated: Jul 28, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics.

Aqib Hasnain1, Shara Balakrishnan2, Dennis M Joshy3

  • 1Department of Mechanical Engineering, University of California Santa Barbara, Santa Barbara, CA, USA. aqib@ucsb.edu.

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|May 30, 2023
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Summary

Researchers developed a machine learning tool to discover gene biomarkers for detecting environmental toxins. This system identifies analyte-responsive promoters, creating a living sensor for malathion detection in real-world conditions.

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Area of Science:

  • Biotechnology and Synthetic Biology
  • Environmental Science
  • Genomics and Bioinformatics

Background:

  • Identifying reliable biomarkers for specific perturbations and metabolites is a significant hurdle in biotechnology and biomanufacturing.
  • Transcriptome-wide analysis of gene expression dynamics is crucial for understanding cellular responses to external stimuli.

Purpose of the Study:

  • To develop a data-driven method for discovering analyte-responsive promoters using transcriptome-wide time-series RNA sequencing data.
  • To create a living biosensor for detecting the organophosphate malathion in environmental samples.
  • To establish a machine learning framework applicable to various host organisms for discovering perturbation-inducible gene expression systems.

Main Methods:

  • A transcriptome-wide approach was employed to rank perturbation-inducible genes from time-series RNA sequencing data.
  • Low-dimensional models of gene expression dynamics were constructed, and genes were ranked using observability analysis to identify cell-state-capturing biomarkers.
  • Synthetic genetic reporters were developed from 15 identified malathion-responsive promoters in Pseudomonas fluorescens SBW25.

Main Results:

  • The study successfully identified 15 analyte-responsive promoters specific to malathion in Pseudomonas fluorescens SBW25.
  • Synthetic genetic reporters demonstrated measurable responses to malathion, enabling its detection.
  • A synthetic consortium approach enhanced malathion reporting, and the system was validated for environmental detection outside the laboratory.

Conclusions:

  • The developed machine learning tool and identified promoters enable the creation of a living sensor for malathion detection.
  • The engineered Pseudomonas fluorescens SBW25 serves as a promising platform for environmental diagnostics.
  • The methodology is adaptable for discovering gene expression systems in diverse host organisms for various applications.