A multi-objective hybrid machine learning approach-based optimization for enhanced biomass and bioactive phycobiliproteins production in Nostoc sp. CCC-403. (June 2021)

• Record Type: Journal Article
• Authors: Saini, Dinesh Kumar; Rai, Amit; Devi, Alka; Pabbi, Sunil; Chhabra, Deepak; Chang, Jo-Shu; Shukla, Pratyoosh
• Is Part Of: Bioresource technology. Volume 329(2021)
• Publication Date: 2021-06
• Subjects: Phycobiliproteins -- Nostoc sp. -- Genetic algorithm -- Machine learning -- genome-scale metabolic network (GSMN); Biomass -- Periodicals; Biomass energy -- Periodicals; Bioremediation -- Periodicals; Agricultural wastes -- Periodicals; Factory and trade waste -- Periodicals; Organic wastes -- Periodicals; Bioénergie -- Périodiques; Déchets agricoles -- Périodiques; Déchets industriels -- Périodiques; Déchets organiques -- Périodiques; Déchets (Combustible) -- Périodiques; 662.88
• DOI: 10.1016/j.biortech.2021.124908
• Languages: English
• ISSNs: 0960-8524

Abstract

Graphical abstract: Highlights: A hybrid machine learning optimization for phycobiliproteins (PBPs) reported. Novel multi-objective based optimization for PBPs in Nostoc sp. CCC 403. An increase of 61.76% (340.5 µg/ml) total PBPs and 90% in dry cell biomass stated. Genome-scale metabolic network (GSMN) clued potential metabolic fluxes for PBPs. 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.