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Fermentation

Fermentation

Most eukaryotic organisms require oxygen to survive and function adequately. Such organisms produce large amounts of energy during aerobic respiration by metabolizing glucose and oxygen into carbon dioxide and water. However, most eukaryotes can generate some energy in the absence of oxygen by anaerobic metabolism.
Fermentation is a type of metabolic process that occurs in the absence of oxygen, where organic molecules such as glucose are broken down to produce energy. During this process, the...

Microbial Fermentation

Microbial Fermentation

Fermentation is a crucial anaerobic metabolic process that enables microbes to derive energy from sugar without relying on oxygen or an electron transport chain. This process is fundamental to various biological and industrial applications and is classified based on the metabolic products generated.Role of Pyruvate in FermentationPyruvate and its derivatives serve as key electron acceptors in fermentative pathways. The oxidation of NADH to regenerate NAD+ is essential for the continuation of...

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Pyruvate is the end product of glycolysis, where glucose is oxidized to pyruvate, simultaneously reducing NAD+ to NADH. Two molecules of ATP are also produced by substrate-level phosphorylation.
In aerobic organisms, pyruvate is metabolized via the citric acid cycle to produce reduced coenzymes NADH and FADH2. These coenzymes are then oxidized in the electron transport chain to produce ATP and, in the process, regenerate the NAD+ and FAD. As seen in some cell types and organisms, fermentation...

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Related Experiment Video

Updated: Jan 8, 2026

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

Fermentation design and process optimization strategy based on machine learning.

Zhen-Zhi Wang¹, Du-Wen Zeng¹, Yi-Fan Zhu¹

¹State Key Laboratory of Microbial Metabolism, and School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.

Biodesign Research

|December 19, 2025

Summary

This summary is machine-generated.

Machine learning enhances fermentation design and optimization for biological manufacturing. This approach optimizes conditions and explores strain potential, driving economic benefits in medicine, food, and bioenergy.

Keywords:

Automated fermentation process control Efficient bioproduction Fermentation optimization Machine learning Process Design

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Last Updated: Jan 8, 2026

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Published on: May 19, 2023

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Published on: March 22, 2022

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

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Published on: December 15, 2017

Area of Science:

Biotechnology and biochemical engineering
Industrial microbiology
Computational biology

Background:

Fermentation is crucial for industrial bioproduction across medicine, food, and bioenergy sectors, offering significant economic advantages.
Strain development is key to fermentation success, but optimizing the fermentation process itself is vital for maximizing engineered strain potential.
Complex factors influence fermentation, necessitating advanced computational tools for effective design and optimization.

Purpose of the Study:

To review the application of machine learning in fermentation design and process optimization.
To highlight the workflow integrating experimental design and machine learning for bioproduction.
To discuss emerging machine learning strategies for advanced fermentation control and analysis.

Main Methods:

Utilizing experimental design strategies to characterize fermentation system performance.
Employing machine learning models for simulating fermentation operations and predicting optimal conditions (medium composition, process parameters).
Exploring advanced machine learning techniques such as automated process control, data mining, transfer learning, hybrid modeling, and soft sensor construction.

Main Results:

Machine learning effectively simulates fermentation systems and identifies optimal operating conditions.
Integration of experimental design and machine learning accelerates the optimization of fermentation processes.
Advanced machine learning strategies expand applications in automated control, strain analysis, and predictive modeling.

Conclusions:

Machine learning is a powerful tool for optimizing complex fermentation systems in industrial bioproduction.
The described workflow enables efficient exploration of genetic potential and enhancement of bioproduction yields.
Emerging machine learning applications promise further advancements in fermentation design, control, and data analysis for biomanufacturing.