Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor
© 2024 Wiley Periodicals LLC..
As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:121 |
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| Enthalten in: |
Biotechnology and bioengineering - 121(2024), 5 vom: 12. Apr., Seite 1583-1595 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Yuan [VerfasserIn] |
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| Links: |
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| Themen: |
Convolutional neural network |
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| Anmerkungen: |
Date Completed 15.04.2024 Date Revised 15.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1002/bit.28661 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM367378558 |
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| 520 | |a As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Escherichia coli | |
| 650 | 4 | |a Raman spectroscopy | |
| 650 | 4 | |a convolutional neural network | |
| 650 | 4 | |a genetic algorithm | |
| 650 | 4 | |a recombinant protein expression | |
| 650 | 4 | |a transfer learning | |
| 650 | 7 | |a Recombinant Proteins |2 NLM | |
| 700 | 1 | |a Zhou, Xiaotian |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Teng |e verfasserin |4 aut | |
| 700 | 1 | |a Luo, An |e verfasserin |4 aut | |
| 700 | 1 | |a Jia, Zhaojun |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Xingquan |e verfasserin |4 aut | |
| 700 | 1 | |a Cai, Weiqi |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Mengge |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xuezhong |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Zhenguo |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Guangzheng |e verfasserin |4 aut | |
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