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DNA Agarose Gel Electrophoresis

DNA Agarose Gel Electrophoresis

Agarose gel electrophoresis is a laboratory technique commonly used to separate DNA fragments by size. However, it can also be used to isolate and purify DNA fragments using a gel extraction protocol.
Gel extraction follows five major steps: running gel electrophoresis to separate fragments, isolating the individual bands, extracting DNA from those bands, and removing the dye and salts from the extracted mixture to obtain pure DNA.
In cloning experiments, both the insert and vector DNA...

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相关实验视频

Updated: May 16, 2025

A G-quadruplex DNA-affinity Approach for Purification of Enzymatically Active G4 Resolvase1

A G-quadruplex DNA-affinity Approach for Purification of Enzymatically Active G4 Resolvase1

Published on: March 18, 2017

从Microbulbifer sp.中优化亚加酶的产生. 使用响应表面方法和机器学习模型.

Lubhan Cherwoo¹, Ritika Dhaneshwar², Parminder Kaur¹

¹Department of Biotechnology, University Institute of Engineering and Technology, Panjab University, Chandigarh, India.

Environmental technology

|April 5, 2025

概括

这项研究优化了微生物 агар酶的产量,用于工业用途,实现了高产量和高活性. 机器学习模型准确地预测了酶生产,提高了 agarase 应用的可扩展性和效率.

关键词:

亚格拉斯亚格拉斯亚格拉斯微型斗牛动物 (Microbulbifer) 是一种有机动物.这样就好了!如果是这样的话,那就好了.机器学习是机器学习.响应表面方法的方法.

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Generation of Fluorescent Protein Fusions in Candida Species

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Published on: December 15, 2017

相关实验视频

Last Updated: May 16, 2025

A G-quadruplex DNA-affinity Approach for Purification of Enzymatically Active G4 Resolvase1

A G-quadruplex DNA-affinity Approach for Purification of Enzymatically Active G4 Resolvase1

Published on: March 18, 2017

Generation of Fluorescent Protein Fusions in Candida Species

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Published on: March 4, 2017

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

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Published on: December 15, 2017

科学领域:

酶学和生物技术酶学和生物技术
微生物发酵过程中的微生物发酵.
生物信息学是一种生物信息学.

背景情况:

酶酶在各种行业中至关重要,包括食品,化品和医药.
目前的阿加拉斯生产方法产量低,成本高,活动不一致.
对于优化微生物来源来实现有效的工业规模的亚加生产,有很大的需求.

研究的目的:

为了优化从微生物源,特别是*Microbulbifer* sp.的细胞外阿加拉酶生产.
为了研究和确定最佳的生长条件,以提高 agarase 产量和活性.
探索机器学习算法,以准确预测阿加拉酶活动.

主要方法:

微生物生长条件的定性和定量分析.
响应表面方法 (RSM) 以优化亚加度,pH,温度和化时间.
机器学习算法的应用和评估,包括辐射基函数神经网络,用于预测建模.

主要成果:

优化条件确定为0.3%的,pH为7,温度为25°C,化时间为36小时,产生317.97μmol min-1的酶活性.
高统计学意义证实F值 (44.75) 和R平方 (0.9827) 进行实验验证.
辐射基函数神经网络表现出卓越的预测性能,R平方为0.989和MSE为0.44.

结论:

优化的生产参数显著提高了 agarase 的可扩展性和效率.
机器学习模型为agarase活动提供了强大而准确的预测.
这些发现支持工业规模的生物反应器运行,并有可能进行实时调整.