Center for Microbial Ecology and Technology (CMET), Ghent University, Coupure Links 653, 9000 Ghent, Belgium; Center for Advanced Process Technology for Urban Resource Recovery (CAPTURE), Frieda Saeysstraat 1, 9052 Ghent, Belgium.
Center for Microbial Ecology and Technology (CMET), Ghent University, Coupure Links 653, 9000 Ghent, Belgium.
Water Res. 2023 Sep 1;243:120323. doi: 10.1016/j.watres.2023.120323. Epub 2023 Jul 9.
As part of the circular bio-economy paradigm shift, waste management and valorisation practices have moved away from sanitation and towards the production of added-value compounds. Recently, the development of mixed culture bioprocess for the conversion of waste(water) to platform chemicals, such as medium chain carboxylic acids, has attracted significant interest. Often, the microbiology of these novel bioprocesses is less diverse and more prone to disturbances, which can lead to process failure. This issue can be tackled by implementing an advanced monitoring strategy based on the microbiology of the process. In this study, flow cytometry was used to monitor the microbiology of lactic acid chain elongation for the production of caproic acid, and assess its performance both qualitatively and quantitatively. Two continuous stirred tank reactors for chain elongation were monitored flow cytometrically for over 336 days. Through community typing, four specific community types could be identified and correlated to both a specific functionality and genotypic diversity. Additionally, the machine-learning algorithms trained in this study demonstrated the ability to predict production rates of, amongst others, caproic acid with high accuracy in the present (R² > 0.87) and intermediate accuracy in the near future (R² > 0.63). The identification of specific community types and the development of predictive algorithms form the basis of advanced bioprocess monitoring based on flow cytometry, and have the potential to improve bioprocess control and optimization, leading to better product quality and yields.
作为循环生物经济范式转变的一部分，废物管理和增值实践已经从卫生处理转向了增值化合物的生产。最近，开发混合培养生物工艺来将废物（水）转化为平台化学品，如中链羧酸，引起了极大的关注。通常情况下，这些新型生物工艺的微生物学多样性较少，更容易受到干扰，从而导致工艺失败。这个问题可以通过实施基于工艺微生物学的先进监测策略来解决。在这项研究中，流式细胞术被用于监测用于生产己酸的乳酸链伸长的微生物学，并定性和定量地评估其性能。两个连续搅拌釜式反应器用于链伸长的流式细胞术监测超过 336 天。通过群落分型，可以识别出四种特定的群落类型，并将其与特定的功能和基因型多样性相关联。此外，本研究中训练的机器学习算法展示了在当前（R²>0.87）和未来中期（R²>0.63）高精度预测己酸等生产速率的能力。特定群落类型的识别和预测算法的开发为基于流式细胞术的先进生物过程监测奠定了基础，并有潜力改善生物过程控制和优化，从而提高产品质量和产量。
