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

Microbial Growth Measurement: Indirect Methods01:27

Microbial Growth Measurement: Indirect Methods

1
Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...
1
Microbial Growth Measurement: Direct Methods01:23

Microbial Growth Measurement: Direct Methods

1
Direct methods for measuring microbial populations in a culture are essential tools in microbiology, providing quantitative data for various applications. Among these, microscopic counts, plate counts, and serial dilution are widely used techniques, each with unique principles and applications.Microscopic CountsMicroscopic counting involves the use of a Petroff-Hausser chamber, a specialized microscope slide with a grid and defined depth. By observing a liquid culture under a microscope,...
1
Green Algae01:21

Green Algae

Green algae, also referred to as chlorophytes, are different from red algae in having the chloroplasts containing chlorophylls a and b, which give them their distinct green hue. However, they lack phycobiliproteins, preventing them from developing the red or blue-green pigmentation seen in red algae. In terms of photosynthetic pigment composition, green algae closely resemble plants and share a close evolutionary relationship with them. Taxonomically Green algae belong to Phylum Chlorophyta in...

You might also read

Related Articles

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

Sort by
Same author

Growth Month-Associated Variation in Volatile Profiles, Anti-Glycation Capacity, and Antioxidant Activity of <i>Cyclocarya paliurus</i> Leaves: A Pilot Study.

Foods (Basel, Switzerland)·2026
Same author

Deceleration-phase restrengthening in compositionally zoned fault materials under transient slip.

Scientific reports·2026
Same author

From design-build-test-learn cycles to AI-driven digital twins for bioprocess scale-up in the Genesis Mission era.

Current opinion in biotechnology·2026
Same author

Design of fluorinated cross-linked poly(ionic liquid) stationary phases for green liquid chromatography and multiple applications in food and drug analysis.

Journal of chromatography. A·2026
Same author

Hot Corrosion of NiCrAlY and NiCrAlY/YSZ Coatings Under Na<sub>2</sub>SO<sub>4</sub> and Na<sub>2</sub>SO<sub>4</sub> + NaCl Salt Deposits at 900 °C.

Materials (Basel, Switzerland)·2026
Same author

Anthropometric trajectories of Korean children and adolescents with severe obesity.

Clinical and experimental pediatrics·2026

Related Experiment Video

Updated: Jun 6, 2025

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
14:33

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Published on: October 1, 2013

14.3K

MAGMA: Microbial and Algal Growth Modeling Application.

Vincent A Xu1, Hakyung Lee1, Bin Long1

  • 1Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.

New Biotechnology
|November 27, 2024
PubMed
Summary

This study introduces MAGMA, a MATLAB software that simplifies creating and validating kinetic models for bioreactor systems. It streamlines bioprocess optimization and data analysis, even integrating AI for literature data extraction.

Keywords:
BioreactorGPT-4oKinetic modelingMicroalgaeODEsRegression

More Related Videos

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.7K
Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases
08:41

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases

Published on: December 19, 2019

10.1K

Related Experiment Videos

Last Updated: Jun 6, 2025

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations
14:33

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Published on: October 1, 2013

14.3K
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.7K
Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases
08:41

Microalgae Cultivation and Biomass Quantification in a Bench-Scale Photobioreactor with Corrosive Flue Gases

Published on: December 19, 2019

10.1K

Area of Science:

  • Biotechnology
  • Biochemical Engineering
  • Computational Biology

Background:

  • Kinetic modeling of biochemical reactions and bioreactor systems is crucial for bioprocess design and optimization.
  • Existing methods for developing kinetic models can be complex and time-consuming.
  • Accurate models enhance understanding of cell culture experiments.

Purpose of the Study:

  • To introduce the Microbial and Algal Growth Modeling Application (MAGMA), a user-friendly MATLAB-based software.
  • To streamline the development, fitting, and validation of kinetic models for bioreactor systems.
  • To demonstrate the integration of AI for automating data extraction from scientific literature.

Main Methods:

  • Development of kinetic models using systems of ordinary differential equations (ODEs).
  • Model fitting through solving inverse problems and statistical evaluation.
  • Application of MAGMA to case studies involving microalgae growth and Rhodococcus jostii fermentation.
  • Proof-of-concept for using OpenAI GPT-4o for graph interpretation to extract time-course data.

Main Results:

  • MAGMA successfully facilitates the creation and validation of kinetic models.
  • The software provides a comprehensive workflow from model building to result visualization.
  • Case studies demonstrate MAGMA's applicability to diverse bioprocesses.
  • Automated data extraction from literature figures shows potential for model calibration.

Conclusions:

  • MAGMA offers an efficient and accessible platform for kinetic modeling in bioprocesses.
  • The software enhances the quantitative analysis of experimental data.
  • Integration of AI capabilities presents a novel approach for model parameterization.
  • MAGMA is open-source and available with MATLAB Runtime, promoting wider adoption.