Biofuser: a multi-source data fusion platform for fusing the data of fermentation process devices.

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Frontiers in digital health Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1390622
Dequan Zhang, Wei Jiang, Jincheng Lou, Xuanzhou Han, Jianye Xia
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Abstract

In the past decade, the progress of traditional bioprocess optimization technique has lagged far behind the rapid development of synthetic biology, which has hindered the industrialization process of synthetic biology achievements. Recently, more and more advanced equipment and sensors have been applied for bioprocess online inspection to improve the understanding and optimization efficiency of the process. This has resulted in large amounts of process data from various sources with different communication protocols and data formats, requiring the development of techniques for integration and fusion of these heterogeneous data. Here we describe a multi-source fusion platform (Biofuser) that is designed to collect and process multi-source heterogeneous data. Biofuser integrates various data to a unique format that facilitates data visualization, further analysis, model construction, and automatic process control. Moreover, Biofuser also provides additional APIs that support machine learning or deep learning using the integrated data. We illustrate the application of Biofuser with a case study on riboflavin fermentation process development, demonstrating its ability in device faulty identification, critical process factor identification, and bioprocess prediction. Biofuser has the potential to significantly enhance the development of fermentation optimization techniques and is expected to become an important infrastructure for artificial intelligent integration into bioprocess optimization, thereby promoting the development of intelligent biomanufacturing.

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Biofuser:用于融合发酵过程设备数据的多源数据融合平台。
近十年来,传统生物工艺优化技术的进步远远落后于合成生物学的快速发展,阻碍了合成生物学成果的产业化进程。近年来,越来越多的先进设备和传感器被应用于生物过程在线检测,以提高对过程的理解和优化效率。这就产生了大量来自不同来源、具有不同通信协议和数据格式的过程数据,需要开发出整合和融合这些异构数据的技术。我们在此介绍一个多源融合平台(Biofuser),该平台旨在收集和处理多源异构数据。Biofuser 将各种数据整合为一种独特的格式,便于数据可视化、进一步分析、模型构建和自动流程控制。此外,Biofuser 还提供额外的应用程序接口,支持使用集成数据进行机器学习或深度学习。我们以核黄素发酵工艺开发的案例研究来说明 Biofuser 的应用,展示其在设备故障识别、关键工艺因素识别和生物工艺预测方面的能力。Biofuser 有潜力显著提升发酵优化技术的发展,有望成为人工智能融入生物过程优化的重要基础设施,从而推动智能生物制造的发展。
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来源期刊
CiteScore
4.20
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊最新文献
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