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 Experiment Videos

Artificial neural network based experimental design procedure for enhancing fermentation development.

J Glassey1, G A Montague, A C Ward

  • 1Department of Chemical and Process Engineering NE1 7RU, United Kingdom.

Biotechnology and Bioengineering
|August 5, 1994
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Innovative transgenic zebrafish biosensor for heavy metal detection.

Environmental pollution (Barking, Essex : 1987)·2024
Same author

Genome mining for the search and discovery of bioactive compounds: the Streptomyces paradigm.

FEMS microbiology letters·2018
Same author

Identification and characterisation of Staphylococcus aureus on low cost screen printed carbon electrodes using impedance spectroscopy.

Biosensors & bioelectronics·2018
Same author

Case Studies in Modelling, Control in Food Processes.

Advances in biochemical engineering/biotechnology·2017
Same author

Vitamin D insufficiency in the first 6 months of infancy and challenge-proven IgE-mediated food allergy at 1 year of age: a case-cohort study.

Allergy·2017
Same author

Granulocyte colony-stimulating factor receptor signalling via Janus kinase 2/signal transducer and activator of transcription 3 in ovarian cancer.

British journal of cancer·2015
Same journal

Minimizing Off-Target Effects of CRISPR-Cas9 With Optimized sgRNA: Evaluation of Efficiency and Specificity in the Tumor Protein 53 (TP53) Region.

Biotechnology and bioengineering·2026
Same journal

Metabolic Flux Analysis Reveals Cell Line-Specific Rewiring in CHO Cells Following TCA Cycle Intermediate Feeding for Bioprocess pH Control.

Biotechnology and bioengineering·2026
Same journal

Photohydrogenotrophic Cultivation of Purple Non-Sulfur Bacteria in an Open Bioreactor: Enhanced Selectivity Through Light Cycling and Ammonium Limitation.

Biotechnology and bioengineering·2026
Same journal

Translating Blue Light Stimulation From Batch to Perfusion: Process and Intracellular Metabolic Analysis.

Biotechnology and bioengineering·2026
Same journal

Nanocarrier-Based Gene Delivery Systems: Mechanisms, Clinical Translation, and Future Perspectives.

Biotechnology and bioengineering·2026
Same journal

Development and Integrated Application of the Multi-Attribute Method (MAM) in Quality Control of Biotechnological Drugs.

Biotechnology and bioengineering·2026
See all related articles

Artificial neural networks offer a novel strategy for optimizing complex fermentation processes, especially in recombinant systems. This approach overcomes limitations of conventional methods, improving bioprocess development.

Area of Science:

  • Biotechnology
  • Biochemical Engineering
  • Computational Biology

Background:

  • Conventional experimental design methods struggle with the inherent nonlinearities and complexities of fermentation processes.
  • Recombinant systems introduce additional challenges, further limiting the effectiveness of traditional optimization techniques.

Purpose of the Study:

  • To present artificial neural networks (ANNs) as a viable alternative strategy for fermentation process development.
  • To demonstrate the application of ANNs in overcoming the limitations of conventional methods in bioprocess optimization.

Main Methods:

  • Utilized artificial neural networks for modeling and optimizing fermentation processes.
  • Developed and presented neural network-based procedures for laboratory application.

Related Experiment Videos

Main Results:

  • Artificial neural networks provide a more effective approach to managing the nonlinearities in bioprocesses.
  • The proposed neural network strategy addresses the complexities associated with recombinant fermentation systems.

Conclusions:

  • Artificial neural networks represent a powerful tool for advancing fermentation process development.
  • This methodology offers a promising alternative for optimizing challenging bioprocesses, particularly in recombinant applications.