Enzyme Technology and Protein Bioinformatics Laboratory, Department of Microbiology, Maharshi Dayanand University, Rohtak 124001, Haryana, India; Centre for Conservation and Utilisation of Blue-Green Algae (CCUBGA), Division of Microbiology, ICAR - Indian Agricultural Research Institute, New Delhi 110 012, India.
Plant Molecular Science Center, Chiba University, Chiba 260-8675, Japan; RIKEN Center for Sustainable Resource Science, Yokohama, Japan.
Bioresour Technol. 2021 Jun;329:124908. doi: 10.1016/j.biortech.2021.124908. Epub 2021 Feb 26.
The cyanobacterial phycobiliproteins (PBPs) are an important natural colorant for nutraceutical industries. Here, a multi-objective hybrid machine learning-based optimization approach was used for enhanced cell biomass and PBPs production simultaneously in Nostoc sp. CCC-403. A central composite design (CCD) was employed to design an experimental setup for four input parameters, including three BG-11 medium components and pH. We achieved a 61.76% increase in total PBPs production and an almost 90% increase in cell biomass by our prediction model. We also established a test genome-scale metabolic network (GSMN) for Nostoc sp. and identified potential metabolic fluxes contributing to PBPs enhanced production. This study highlights the advantage of the hybrid machine learning approach and GSMN to achieve optimization for more than one objective and serves as the foundation for future efforts to convert cyanobacteria as an economically viable source for biofuels and natural products.
蓝藻藻胆蛋白（PBPs）是营养保健品行业的一种重要天然色素。在这里，我们采用了一种基于多目标混合机器学习的优化方法，同时提高了念珠藻 CCC-403 的细胞生物量和 PBPs 的产量。我们使用中心复合设计（CCD）来设计实验设置，其中包含四个输入参数，包括 BG-11 培养基的三个成分和 pH 值。通过我们的预测模型，我们实现了总 PBPs 产量提高 61.76%，细胞生物量提高近 90%。我们还为念珠藻建立了一个测试基因组规模代谢网络（GSMN），并确定了有助于 PBPs 产量提高的潜在代谢通量。这项研究强调了混合机器学习方法和 GSMN 的优势，可用于实现多个目标的优化，为将来将蓝藻转化为生物燃料和天然产物的经济可行来源奠定了基础。
