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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
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...
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...
Bioreactor Controls-II01:18

Bioreactor Controls-II

In aerobic fermentations, oxygen is vital for microbial growth and metabolite production. Since air comprises only about 20% oxygen and the gas is poorly soluble in water—just 9 ppm at 20°C—supplying sufficient oxygen becomes a critical challenge, especially in high-demand processes like yeast growth or citric acid production. Even a fully saturated broth may offer only a few seconds of oxygen availability.To address this, sterile or scrubbed air is introduced into the fermentor via a sparger...

You might also read

Related Articles

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

Sort by
Same author

Robust Cell Segmentation for Size Distribution Estimation via Synthetic-Data Training.

Biotechnology journal·2026
Same author

An Integrated Experimental and Modeling Approach for Crystallization of Complex Biotherapeutics.

Crystal growth & design·2025
Same author

Multidose transient transfection of human embryonic kidney 293 cells modulates recombinant adeno-associated virus2/5 Rep protein expression and influences the enrichment fraction of filled capsids.

Biotechnology and bioengineering·2024
Same author

Droplet-Based Evaporative System for the Estimation of Protein Crystallization Kinetics.

Crystal growth & design·2021
Same author

Model-based control for column-based continuous viral inactivation of biopharmaceuticals.

Biotechnology and bioengineering·2021
Same author

Crystallization of a nonreplicating rotavirus vaccine candidate.

Biotechnology and bioengineering·2021

Related Experiment Video

Updated: Jul 3, 2026

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

LLM-Guided Parameter Optimization for Mechanistic CHO Cell Bioreactor Models.

Han Bit Kim1, Janghan Lee2,3, Seo-Yeon Kim2,3

  • 1Department of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea.

Biotechnology and Bioengineering
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework using large language models (LLMs) to improve parameter estimation for Chinese Hamster Ovary (CHO) cell culture models. The approach enhances bioprocess modeling efficiency and reliability by overcoming optimization challenges.

Keywords:
CHO cellsbioreactor modelinglarge language models (LLMs)mechanistic modelingparameter estimation

More Related Videos

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

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

Related Experiment Videos

Last Updated: Jul 3, 2026

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

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

Area of Science:

  • Biotechnology
  • Computational Biology
  • Process Engineering

Background:

  • Mechanistic models are crucial for Chinese Hamster Ovary (CHO) cell bioprocess development.
  • Parameter estimation in these models is challenging due to complexity and biological variability.
  • Inefficient search spaces and local minima hinder accurate model calibration.

Purpose of the Study:

  • To develop a structure-aware optimization framework for enhanced bioprocess model parameter estimation.
  • To integrate large language model (LLM) reasoning into the parameter estimation loop.
  • To improve the efficiency and robustness of mechanistic models for CHO cell cultures.

Main Methods:

  • Proposed a framework integrating LLM reasoning with parameter estimation.
  • LLM analyzed model equations and data discrepancies to guide parameter boundary adjustments.
  • Combined LLM insights with correlation-based scaling and stochastic global search (LHS, CMA-ES).

Main Results:

  • The framework successfully escaped local minima in parameter optimization.
  • Accurately resolved complex, nonlinear metabolite dynamics, including lactate and ammonium shifts.
  • Demonstrated consistent reduction in prediction error and improved optimization robustness across multiple batches.

Conclusions:

  • Integrating structure-aware LLM guidance with numerical optimization significantly improves bioprocess modeling.
  • The proposed framework enhances the reliability and efficiency of mechanistic models for CHO cell cultures.
  • This approach offers a promising solution for critical challenges in bioprocess development and control.