BIOS: The bio-intelligent DBTL cycle, a key enabler catalysing the industrial transformation towards sustainable biomanufacturing

化石资源的使用导致大气中二氧化碳含量增加和全球气候变化，应迅速被实施循环经济所取代。循环生物经济在适度操作的生物过程中转化可持续基质提供了大量的解决方案。虽然合成生物学为菌株工程提供了大量的工具，但在工业条件下将它们快速应用于宿主以获得最佳性能仍然具有挑战性。有前途的创新往往被困在“死亡之谷”，因为应变工程面临着一个过于复杂的假定操作空间。需要新的方法来提高菌株和生物工艺工程的速度和成功率。生物智能方法，严格应用于BIOS，旨在加速和改善传统的“设计-构建-测试-学习”（DBTL）周期的菌株和生物过程工程。跨学科的合作将连接微生物学、分子生物学、生物化学工程与信息学、自动化工程和机械工程。新的创新指标，生物传感器和生物致动器被开发用于生物技术接口的双向通信。数字双胞胎是模仿细胞和过程水平创建的。集成人工智能不仅可以提高预测质量，还可以实现混合学习，这是提高新型生物智能DBTL周期（biDBTL）速度和成功率的关键原因。biDBTL的力量将通过创建产生萜烯、聚烯烃和甲基丙烯酸酯的恶臭杆菌菌株来展示。所有这些都是极具吸引力的产品，具有减少人为温室气体足迹的巨大潜力。BIOS将为应变和工艺工程打开一扇去中心化、网络化协作的大门，有效地将个人专业知识联系起来，以实现共生和快速发展。BIOS还通过实现自主、自我控制的生物过程，为去中心化的生物制造铺平了道路。

The usage of fossil resources leading to increasing atmospheric CO2 levels and global climate change should be rapidly replaced by implementing a circular economy. Circular bioeconomy converting sustainable substrates in moderately operating bioprocesses offers a plenitude of solutions. While synthetic biology provides a multitude of tools for strain engineering, their rapid use in hosts for optimal performance under industrial conditions is still challenging. Promising innovations are often trapped in the ‘valley-of-death’ as strain engineering faces a too complex space of putative manipulations. Novel approaches are needed to increase speed and success rate of strain and bioprocess engineering. The bio-intelligent approach, rigorously applied in BIOS, aims to accelerate and improve the conventional ‘design-build-test-learn’ (DBTL) cycle for strain and bioprocess engineering. Interdisciplinary collaboration will bridge microbiology, molecular biology, biochemical engineering with informatics, automation engineering, and mechanical engineering. Novel innovative metrics, biosensors, and bioactuators are developed for bi-directionally communication at biological-technical interfaces. Digital twins are created mimicking cellular and process levels. Integrating AI not only improves prediction quality but also enables hybrid learning, the key reason to increase speed and success rate in the novel bio-intelligent DBTL cycle (biDBTL). The power of biDBTL will be showcased by creating P. putida producer strains for terpenes, polyolefines, and methylacrylate. All are highly attractive products with a high potential for reducing anthropogenic greenhouse footprint. BIOS will open the door to a de-centralized, networked collaboration for strain and process engineering that efficiently links individual expertise for the sake of a symbiotic and rapid progress. BIOS also paves the way to de-centralized bio-manufacturing by implementing autonomous, self-controlled bioprocesses.