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

Bioreactor Controls-III01:22

Bioreactor Controls-III

Strain improvement is a foundational strategy in industrial microbiology aimed at maximizing microbial productivity, particularly because natural isolates typically yield commercially valuable products in very low concentrations. Although optimizing the culture medium and environmental conditions can improve yields, these adjustments are inherently limited by the organism’s genetic potential. As a result, the focus shifts toward genetic modifications to enhance biosynthetic capacity. The...
Designing Growth Media for Bioreactors01:30

Designing Growth Media for Bioreactors

Growth media provide essential nutrients that support cell growth and metabolism, thereby enhancing the yield of valuable products such as enzymes, antibiotics, and biomass. Designing an effective growth medium involves balancing all components to prevent nutrient limitations or toxic excesses, both of which can impair growth and reduce product yields.Composition of a Typical Growth MediumA typical growth medium contains carbon and nitrogen sources, salts, vitamins, trace elements, and...

You might also read

Related Articles

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

Sort by
Same author

Hydrophobic interaction chromatography resolves extracellular vesicle fractions with distinct lipidomic signatures.

Scientific reports·2026
Same author

Improved preanalytical workflow for pancreatic tissue lipidomics: insights into lipid stability and polar lipid recovery.

Journal of lipid research·2025
Same author

IPEC-J2 as a cellular model for studying intestinal mucus.

Scientific reports·2025
Same author

Fish-derived lipids directly stimulate NK cell activity and IFN-γ synthesis: A novel dietary intervention strategy to enhance antiviral immunity in humans.

Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie·2025
Same author

A global perspective on exposure and data gaps for microplastic contaminants in bivalves and cephalopods.

Marine pollution bulletin·2025
Same author

Evaluating Novel Direct Injection Liquid Chromatography-Mass Spectrometry Method and Extraction-Based Workflows for Untargeted Lipidomics of Extracellular Vesicles.

Journal of proteome research·2025

Related Experiment Video

Updated: Jul 25, 2026

Cultivation of Green Microalgae in Bubble Column Photobioreactors and an Assay for Neutral Lipids
11:08

Cultivation of Green Microalgae in Bubble Column Photobioreactors and an Assay for Neutral Lipids

Published on: January 7, 2019

21.0K

Microbe cultivation guidelines to optimize rhamnolipid applications.

Ilona E Kłosowska-Chomiczewska1, Adam Macierzanka2, Karol Parchem3

  • 1Department of Colloid and Lipid Science, Faculty of Chemistry, Gdańsk University of Technology, 11/12 G. Narutowicza St., 80-233, Gdańsk, Poland. ilochomi@pg.edu.pl.

Scientific Reports
|April 10, 2024
PubMed
Summary

Developing computational models aids in selecting microbial surfactants, like rhamnolipids, for specific applications. These predictive tools streamline biosurfactant selection based on biosynthesis and solubilization data.

Keywords:
Biosurfactant designMicellar solubilizationMicrobial cultivationQSPRRhamnolipid biosynthesis

More Related Videos

Enhanced Oil Recovery using a Combination of Biosurfactants
13:19

Enhanced Oil Recovery using a Combination of Biosurfactants

Published on: June 3, 2022

5.2K
Author Spotlight: Understanding Rhamnolipid Regulation in Pseudomonas aeruginosa
04:37

Author Spotlight: Understanding Rhamnolipid Regulation in Pseudomonas aeruginosa

Published on: March 29, 2024

1.3K

Related Experiment Videos

Last Updated: Jul 25, 2026

Cultivation of Green Microalgae in Bubble Column Photobioreactors and an Assay for Neutral Lipids
11:08

Cultivation of Green Microalgae in Bubble Column Photobioreactors and an Assay for Neutral Lipids

Published on: January 7, 2019

21.0K
Enhanced Oil Recovery using a Combination of Biosurfactants
13:19

Enhanced Oil Recovery using a Combination of Biosurfactants

Published on: June 3, 2022

5.2K
Author Spotlight: Understanding Rhamnolipid Regulation in Pseudomonas aeruginosa
04:37

Author Spotlight: Understanding Rhamnolipid Regulation in Pseudomonas aeruginosa

Published on: March 29, 2024

1.3K

Area of Science:

  • Biotechnology and biochemical engineering
  • Surfactant chemistry
  • Computational modeling

Background:

  • Selecting natural surfactants for specific applications is challenging due to complex production variables.
  • Knowledge on microbial surfactants, particularly rhamnolipids (RLs), is often limited to specific experimental conditions.
  • Triglyceride (TG) solubilization by RLs is an underrepresented area in current literature.

Purpose of the Study:

  • To develop a computational framework for biosynthesizing rhamnolipids with targeted properties.
  • To create predictive models for rhamnolipid characteristics and solubilization efficiency.
  • To facilitate the selection of optimal biosurfactants for various applications.

Main Methods:

  • Amassed literature data on RL biosynthesis and micellar solubilization.
  • Augmented literature data with experimental results on triglyceride solubilization.
  • Constructed mathematical models (logPRL and logMSR) to predict RL properties and performance.

Main Results:

  • Developed predictive models with robust R2 values (0.581-0.997 for RL characteristics, 0.804 for solubilization efficiency).
  • Identified key descriptors influencing RL properties and solubilization, ranking their impact.
  • Translated models into user-friendly calculators for biosurfactant selection.

Conclusions:

  • Computational models can effectively predict rhamnolipid characteristics and solubilization efficiency.
  • The developed calculators streamline the process of selecting appropriate microbial biosurfactants.
  • This approach bridges the gap between scientific knowledge and practical application of microbial surfactants.