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

Time delay in simple chemostat models.

N MacDonald

    Biotechnology and Bioengineering
    |June 1, 1976
    PubMed
    Summary
    This summary is machine-generated.

    Adding time delays to chemostat models reveals instability and limit cycles. This study compares model results with experimental data, explaining discrepancies in biomass and cell number damping.

    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

    FFEM-DB 2.0: an updated and extended spatio-temporal dataset of flood fatalities occurred from 1980 to 2024 in the Euro-Mediterranean region.

    Scientific data·2026
    Same author

    The safety and efficacy of day-case total joint arthroplasty.

    Annals of the Royal College of Surgeons of England·2021
    Same author

    The accuracy of pre-operative (P)-POSSUM scoring and cardiopulmonary exercise testing in predicting morbidity and mortality after pancreatic and liver surgery: A systematic review.

    Annals of medicine and surgery (2012)·2021
    Same author

    The building backlog of NHS elective cases post Covid-19.

    The British journal of surgery·2020
    Same author

    Laparoscopic Ovarian Transposition and Ovariopexy for Fertility Preservation in Patients Treated with Pelvic Radiotherapy with or without Chemotherapy.

    Facts, views & vision in ObGyn·2020
    Same author

    Integration of the Duke Activity Status Index into preoperative risk evaluation: a multicentre prospective cohort study.

    British journal of anaesthesia·2019
    Same journal

    Growth Model for Continuous Culture of a Hydrogen-Oxidizing Bacterium, Hydrogenophilus thermoluteolus Strain TH-1.

    Biotechnology and bioengineering·2026
    Same journal

    Glycoengineered Recombinant Alpha1-Antitrypsin Results in Comparable In Vitro and In Vivo Activities to Human Plasma-Derived Protein.

    Biotechnology and bioengineering·2026
    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
    See all related articles

    Area of Science:

    • Microbial Physiology
    • Biochemical Engineering
    • Mathematical Modeling

    Background:

    • Established chemostat models by Monod and Williams predict strongly damped transients.
    • Chemostat dynamics are crucial for understanding microbial growth and productivity.
    • Experimental data sometimes shows weaker damping than models predict.

    Purpose of the Study:

    • To incorporate time delays into Monod and Williams chemostat models.
    • To analyze the impact of time delays on model stability and transient behavior.
    • To reconcile model predictions with experimental observations of biomass and cell number damping.

    Main Methods:

    • Modified Monod and Williams chemostat models to include time delays.
    • Performed stability analysis to determine conditions for instability and limit cycle formation.

    Related Experiment Videos

  • Compared model-generated transient responses with existing experimental data.

    • Main Results:

    • Incorporating time delays can destabilize the equilibrium point in chemostat models.
    • Longer time delays lead to unstable equilibria and the emergence of limit cycles.
    • Williams' model with moderate time delays produced weakly damped transients, aligning with experimental cell number data but not biomass data.

    Conclusions:

    • Time delays are a critical factor influencing chemostat dynamics.
    • Model-based predictions of transient damping can differ from experimental results, particularly for biomass.
    • Further refinement of chemostat models is needed to fully capture observed microbial population dynamics.