Zhang Dequan, Jiang Wei, Lou Jincheng, Han Xuanzhou, Xia Jianye
State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China.
Engineering Biology for Biomanufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China.
Front Digit Health. 2024 Oct 21;6:1390622. doi: 10.3389/fdgth.2024.1390622. eCollection 2024.
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.
在过去十年中，传统生物过程优化技术的进展远远落后于合成生物学的快速发展，这阻碍了合成生物学成果的产业化进程。最近，越来越多先进的设备和传感器被应用于生物过程的在线检测，以提高对过程的理解和优化效率。这导致了来自各种不同通信协议和数据格式的大量过程数据，需要开发整合和融合这些异构数据的技术。在此，我们描述了一个多源融合平台（Biofuser），其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、模型构建和自动过程控制。此外，Biofuser还提供了额外的应用程序编程接口，支持使用整合后的数据进行机器学习或深度学习。我们通过一个关于核黄素发酵过程开发的案例研究来说明Biofuser的应用，展示了其在设备故障识别、关键过程因素识别和生物过程预测方面的能力。Biofuser有潜力显著提升发酵优化技术的发展，并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。
