Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

PD Controller: Design

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...

You might also read

Related Articles

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

Sort by

Same author

iAstrocytes model cytokine influences on complement expression and neuronal network synchronization.

bioRxiv : the preprint server for biology·2026

Same author

A review of the State-of-the-Art: progress in ultrasonic and acoustic techniques for quality assessment in the development and manufacturing of oral solid dosage forms - Part II: Applications and emerging directions.

International journal of pharmaceutics·2026

Same author

CPGPrompt: translating clinical guidelines into large language model-executable decision support.

Journal of the American Medical Informatics Association : JAMIA·2026

Same author

The nature of thallium crystals in Brassica oleracea (kale): a synchrotron multi-technique investigation.

Metallomics : integrated biometal science·2026

Same author

Comparative Effectiveness and Risk of Severe Infection in Adult Patients With MS Treated With Diroximel Fumarate Versus Anti-CD20 Monoclonal Antibodies: A Real-World Claims Analysis.

Advances in therapy·2026

Same author

Targeting Androgen Receptor as a Novel Radiosensitizing Therapy to Improve Long-Term Survival and Anti-tumor Immunity in Glioblastoma via TGF-β/Smad3 Axis Reprogramming.

Research square·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

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

Search research articles

Related Experiment Video

Updated: Jul 24, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Machine learning-based model predictive controller design for cell culture processes.

Mohammad Rashedi¹, Mina Rafiei¹, Matthew Demers²

¹Operations Digital Strategy & Capabilities, Amgen Inc., Thousand Oaks, California, USA.

Biotechnology and Bioengineering

|July 3, 2023

Summary

This summary is machine-generated.

This study introduces a model predictive controller (MPC) using machine learning to optimize fed-batch cell cultures for biopharmaceutical production. The approach enhances cell growth and metabolite output, improving cost-effectiveness and product reliability.

Keywords:

bio-pharmaceutical process gaussian process machine learning model predictive control neural network mpc optimal control

More Related Videos

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Published on: September 28, 2018

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

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

Published on: May 18, 2020

Related Experiment Videos

Last Updated: Jul 24, 2025

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Use of High-Throughput Automated Microbioreactor System for Production of Model IgG1 in CHO Cells

Published on: September 28, 2018

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

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

Published on: May 18, 2020

Area of Science:

Biopharmaceutical Manufacturing
Bioprocess Engineering
Control Systems

Background:

Biopharmaceutical production requires optimizing critical quality attributes for reliability and cost-effectiveness.
Scalable and optimal control strategies are essential for meeting process constraints and objectives in cell culture.
Existing physics-based models often lack high fidelity for complex cell culture processes.

Purpose of the Study:

To develop an optimal feeding strategy for fed-batch cell culture processes using a model predictive controller (MPC).
To maximize cell growth and metabolite production, specifically daily protein yield.
To integrate machine learning algorithms into the MPC for improved forecasting and control.

Main Methods:

Utilized a model predictive controller (MPC) for computing optimal feeding strategies.
Employed machine learning algorithms including linear regression, Gaussian processes, and neural networks for the forecast model.
Developed linear and nonlinear models based on real cell culture process data.
Evaluated controller performance through real-time experiments.

Main Results:

The MPC successfully computed an optimal feeding strategy to maximize cell growth and metabolite production.
Machine learning models aided in developing high-fidelity forecast models for complex cell culture dynamics.
The control scheme maintained all metabolites and process variables within specified limits.
Real-time experiments validated the effectiveness of the designed controllers.

Conclusions:

Model predictive control integrated with machine learning provides an effective strategy for optimizing fed-batch cell culture processes.
This approach enhances biopharmaceutical production by maximizing protein yield and ensuring process stability.
The developed models and control scheme offer a scalable solution for improving the reliability and cost-effectiveness of biopharmaceutical manufacturing.