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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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Related Experiment Video

Updated: Oct 8, 2025

Analyzing Ex Vivo Metabolic Flux in Splenic and Cardiac Macrophages and Bone Marrow Monocytes
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Understanding Metabolic Flux Behaviour in Whole-Cell Model Output.

Sophie Landon1,2, Oliver Chalkley1,2,3, Gus Breese2

  • 1BrisSynBio, University of Bristol, Bristol, United Kingdom.

Frontiers in Molecular Biosciences
|January 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new pipeline to analyze complex whole-cell model data. It uses machine learning to visualize metabolic reaction fluxes, aiding in understanding gene knockout effects and designing genomes.

Keywords:
machine learningnetworkssnorkeltime seriesweak learningwhole-cell modelling

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Synthetic Biology

Background:

  • Whole-cell modeling is an emerging field with applications in drug discovery and experimental design.
  • Interpreting the high-dimensional output of whole-cell models is challenging.

Purpose of the Study:

  • To develop and present an analysis pipeline for interpreting whole-cell model metabolic reaction fluxes.
  • To visualize and understand the effects of single gene knockouts in *Mycoplasma genitalium*.

Main Methods:

  • Combined machine learning, dimensionality reduction, and network analysis.
  • Simulated single gene knockouts in a *Mycoplasma genitalium* whole-cell model.
  • Analyzed metabolic reaction fluxes and their correlation with phenotypic outputs.

Main Results:

  • Identified trends in reaction behaviors correlating with phenotypic classes.
  • Highlighted specific cellular subsystems affected by gene knockouts.
  • Discovered key reactions that serve as markers for phenotypic classes within the metabolic network.

Conclusions:

  • The analysis pipeline aids in understanding complex *in silico* cells and gene knockout effects.
  • Facilitates genome design by providing insights into metabolic network behavior.
  • Supports the growing use and application of whole-cell models.