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Updated: Nov 14, 2025
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
Published on: December 15, 2017
A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive
Dinesh Kumar Saini1, Amit Rai2, Alka Devi3
1Enzyme 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.
Abstract:
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.
