相关实验视频

Updated: Aug 13, 2026

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

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

Published on: December 15, 2017

通过使用深度神经网络的高吞吐量管道优化Escherichia coli生产蛋白质的介质组件

Kazuki Watanabe¹, Tai-Ying Chiou², Masaaki Konishi²

¹Department of Biotechnology and Environmental Chemistry, Graduate School of Engineering, Kitami Institute of Technology, 165 Koen-cho Kitami, Hokkaido 090-8507, Japan.

Journal of bioscience and bioengineering

|January 31, 2024

概括

一个新的管道迅速优化了大肠杆菌介质以增强绿色光蛋白表达,使用深度神经网络和数学算法,实现了两倍以上的改进.

科学领域:

生物技术是生物技术.
分子生物学分子生物学
生物信息学是一种生物信息学.

背景情况:

优化微生物表达系统对于产生绿色光蛋白 (GFP) 等重组蛋白质至关重要.
传统的媒介优化方法往往耗时,可能无法达到最大收益率.
大肠杆菌是用于重组蛋白质生产的广泛使用的宿主.

研究的目的:

开发和应用一个快速的,单周期优化管道,以增强大肠杆菌中的GFP表达.
通过深度神经网络 (DNN) 和数学优化,同时优化 18 个介质组件.
为了比较数据采样方法,直角数组 (OA) 和拉丁超立方体采样 (LHS),用于DNN模型开发.

主要方法:

采用了将DNN与数学优化算法 (贝叶斯优化,遗传算法) 集成的管道.
两种数据采样策略,OA和LHS,用于生成初始媒体组合.
在实验数据上训练DNN模型,验证,并分析组件灵敏度.
使用K-means集群来分组组件灵敏度并选择代表性模型.
优化介质 (OM) 组合被识别并通过培养验证.

主要成果:

无论是OA还是LHS采样方法,都使得DNN模型的估计准确度足够高 (平均平方误差为0.015-0.64).
从两种采样方法中获得的优化介质 (OMs) 显著增强了GFP光.
最好的OMs与初始学习数据相比,实现了2.12倍 (OA) 和2.13倍 (LHS) 的更高表达.
组件灵敏度被有效地分组,允许代表性模型选择.

结论:

开发的单周期优化管道,将DNN和数学算法结合起来,对于快速的媒介优化是有效的.
该管道成功增强了大肠杆菌中的GFP表达,证明了其实际适用性.
这项研究突出了数据驱动方法优化生物过程的潜力.

关键词:

人工智能的人工智能是人工智能.贝叶斯的优化是贝叶斯的优化.深度神经网络是一种深度神经网络.设计实验的设计.埃舍里希亚大肠杆菌 (Escherichia coli) 是一个大肠杆菌.遗传算法遗传算法遗传算法绿色光蛋白质是一种绿色光蛋白质.

更多相关视频

Escherichia coli-Based Cell-Free Protein Synthesis: Protocols for a robust, flexible, and accessible platform technology

Escherichia coli-Based Cell-Free Protein Synthesis: Protocols for a robust, flexible, and accessible platform technology

Published on: February 25, 2019

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

相关概念视频

Bioreactor Controls-III

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...

Methods of Medium Optimization

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...

Upstream Processing

Upstream Processing

Upstream processing represents a critical phase in biomanufacturing, wherein biological systems such as microorganisms, mammalian cells, or insect cells are cultivated to produce therapeutic proteins, vaccines, enzymes, or other biologically derived products. This phase encompasses all steps from the selection and genetic manipulation of the production organism to the cultivation of cells in bioreactors under tightly controlled environmental conditions.Host Selection and Genetic OptimizationThe...