Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Prokaryotic Transcriptional Activators and Repressors01:58

Prokaryotic Transcriptional Activators and Repressors

22.3K
The organization of prokaryotic genes in their genome is notably different from that of eukaryotes. Prokaryotic genes are organized, such that the genes for proteins involved in the same biochemical process or function are located together in groups. This group of genes, along with their regulatory elements, are collectively known as an operon. The functional genes in an operon are transcribed together to give a single strand of mRNA known as polycistronic mRNA.
Transcription of prokaryotic...
22.3K
Coordination of Gene Expression Processes in Bacteria01:29

Coordination of Gene Expression Processes in Bacteria

169
The DNA replication, transcription, and translation processes are intricately coupled in bacteria, allowing efficient gene expression and rapid protein synthesis. While this physical and functional coordination is advantageous, it introduces challenges that bacteria overcome through specific regulatory mechanisms.Coupling of Replication, Transcription, and TranslationThe coupling of replication, transcription, and translation is a hallmark of bacterial gene expression. As the replisome unwinds...
169
Gene Regulation During Sporulation01:17

Gene Regulation During Sporulation

88
Sporulation is a complex developmental process that allows certain Gram-positive bacteria, such as Bacillus subtilis and Clostridium species, to survive extreme environmental conditions. This process is tightly regulated by a series of signaling cascades and transcriptional controls, ensuring the formation of a highly resistant endospore.Sporulation is triggered by unfavorable conditions, such as nutrient depletion, and is governed by a phosphorelay system. One of the sensor kinases, such as...
88
Operon Model01:23

Operon Model

134
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...
134
Biosynthesis in Bacteria01:24

Biosynthesis in Bacteria

124
Biosynthesis in bacteria is a fundamental anabolic process that generates essential macromolecules, including proteins, nucleic acids, lipids, and polysaccharides. These macromolecules are critical for cellular growth, replication, and function. The process is tightly regulated and energetically linked to catabolic pathways to ensure optimal resource utilization.Biosynthetic pathways begin with precursor metabolites such as pyruvate, acetyl-CoA, and glucose-6-phosphate derived from glycolysis,...
124
Stringent Response in E. coli01:23

Stringent Response in E. coli

62
Bacterial growth is closely tied to nutrient availability, with cells proliferating exponentially under favorable conditions and entering a stationary phase when resources become scarce. This transition is mediated by a regulatory mechanism known as the stringent response, which allows bacteria to adapt to nutrient deprivation by modulating gene expression and metabolic activity.During nutrient scarcity, intracellular amino acid levels decline. It results in the accumulation of uncharged tRNAs...
62

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A pan-viral map of host dependency factors from multi-omics integration and machine learning across influenza A, SARS-CoV-2, Zika, and dengue viruses.

Journal of translational medicine·2026
Same author

Slimformer: An NLP-based web server for semantic categorization of gene sets.

Computational and structural biotechnology journal·2025
Same author

Correction: Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control.

PloS one·2025
Same author

Molecular docking and molecular dynamics simulation studies of inhibitor candidates against <i>Anopheles gambiae</i> 3-hydroxykynurenine transaminase and implications on vector control.

Heliyon·2025
Same author

Combination of computational techniques and RNAi reveal targets in Anopheles gambiae for malaria vector control.

PloS one·2024
Same author

Correction: Heuristic-enabled active machine learning: A case study of predicting essential developmental stage and immune response genes in Drosophila melanogaster.

PloS one·2024

Related Experiment Video

Updated: Sep 20, 2025

Plant-Microbe Interaction: Transcriptional Response of Bacillus Mycoides to Potato Root Exudates
08:59

Plant-Microbe Interaction: Transcriptional Response of Bacillus Mycoides to Potato Root Exudates

Published on: July 2, 2018

12.7K

Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis.

Kulwadee Thanamit1, Franziska Hoerhold1, Marcus Oswald1

  • 1Systems Biology Research Group, Institute for Infectious Diseases and Infection Control (IIMK), Jena University Hospital, Kollegiengasse 10, 07743, Jena, Germany.

BMC Bioinformatics
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

We developed a new method, Linear Programming based Gene Expression Model (LPM-GEM), to estimate metabolic fluxes using gene expression data. This approach offers a cost-effective alternative to traditional 13C tracer experiments, improving metabolic flux analysis.

Keywords:
Bacillus subtilisCarbon sourceConstraint-based modelingFlux balance analysisMixed-integer linear programmingThermodynamically infeasible loopsTranscriptomics

More Related Videos

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments
20:28

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

Published on: October 2, 2012

14.2K
Monitoring Intraspecies Competition in a Bacterial Cell Population by Cocultivation of Fluorescently Labelled Strains
06:45

Monitoring Intraspecies Competition in a Bacterial Cell Population by Cocultivation of Fluorescently Labelled Strains

Published on: January 18, 2014

8.7K

Related Experiment Videos

Last Updated: Sep 20, 2025

Plant-Microbe Interaction: Transcriptional Response of Bacillus Mycoides to Potato Root Exudates
08:59

Plant-Microbe Interaction: Transcriptional Response of Bacillus Mycoides to Potato Root Exudates

Published on: July 2, 2018

12.7K
A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments
20:28

A Toolkit to Enable Hydrocarbon Conversion in Aqueous Environments

Published on: October 2, 2012

14.2K
Monitoring Intraspecies Competition in a Bacterial Cell Population by Cocultivation of Fluorescently Labelled Strains
06:45

Monitoring Intraspecies Competition in a Bacterial Cell Population by Cocultivation of Fluorescently Labelled Strains

Published on: January 18, 2014

8.7K

Area of Science:

  • Systems Biology
  • Metabolic Engineering
  • Computational Biology

Background:

  • Cellular metabolism is crucial for biotechnology, synthetic biology, and health sciences.
  • 13C tracer experiments are accurate but resource-intensive for metabolic flux analysis.
  • Flux Balance Analysis (FBA) offers a low-cost alternative but is less informative; integrating gene expression data can improve its accuracy.

Purpose of the Study:

  • To introduce a novel method, Linear Programming based Gene Expression Model (LPM-GEM), for estimating metabolic fluxes.
  • To integrate gene expression data into FBA models efficiently.
  • To provide an alternative to 13C tracer experiments for metabolic flux estimation.

Main Methods:

  • Developed LPM-GEM, which linearly embeds gene expression data into FBA constraints.
  • Implemented strategies to reduce thermodynamically infeasible loops in omics-based model building.
  • Built and validated a *B. subtilis* metabolic model using eight different carbon sources.

Main Results:

  • LPM-GEM achieved good metabolic flux predictions when validated with 13C tracer data.
  • The model accurately predicted specific carbon source utilization.
  • LPM-GEM demonstrated strong prediction performance on unseen datasets and outperformed existing methods.

Conclusions:

  • LPM-GEM efficiently integrates gene expression data for metabolic flux estimation.
  • The method supports gene expression-based FBA models.
  • LPM-GEM is a viable alternative to tracer experiments, especially when they are not feasible.