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Microalgae are photosynthetic eukaryotes that serve as microbial cell factories for the production of useful biochemicals, including pigments. These pigments are eco-friendly alternatives to synthetic dyes and reduce environmental and health risks. They also exhibit excellent anti-oxidative properties, making them a useful commodity in the nutrition and pharmaceutical industries. Light-harvesting pigments such as chlorophylls and phycobilins, and photoprotective carotenoids are some of the most common microalgal pigments. The increasing demand for these pigments in industrial applications has prompted a need to improve their metabolic yield in microalgal cells. So far, expensive cultivation methods and sensitivity to microbial contamination remain the main obstacles to the large-scale production of these pigments. This review highlights current issues and future prospects related to the production of microalgal pigments. The review also emphasizes the use of engineering approaches such as genetic engineering, and optimization of media components and physical parameters to increase their commercial-scale production.

Eukaryotic microalgae are a rich source of commercially important metabolites including lipids, pigments, sugars, amino acids and enzymes. However, their inherent genetic potential is usually not enough to support high level production of metabolites of interest. In order to move on from the traditional approach of improving product yields by modification of the cultivation conditions, understanding the metabolic pathways leading to the synthesis of the bioproducts of interest is crucial. Identification of new targets for strain engineering has been greatly facilitated by the rapid development of high-throughput sequencing and spectroscopic techniques discussed in this review. Despite the availability of high throughput analytical tools, examples of gathering and application of proteomic and metabolomic data for metabolic engineering of microalgae are few and mainly limited to lipid production. The present review highlights the application of contemporary proteomic and metabolomic techniques in eukaryotic microalgae for redesigning pathways for enhanced production of algal metabolites.

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.

The production of phycobiliproteins (PBPs) from cyanobacteria represents both the industrial application and their commercial value. In this study, the capability of Anabaena variabilis CCC421 for the production of PBPs was evaluated which was further improved by optimization of selected BG-11 medium components viz. FAC, K2HPO4 and trace metals. A design matrix approach using evolutionary algorithm comprised of genetic-algorithm (GA) and fuzzy-logic-methodology (FLM), i.e., GA-Fuzzy, was used for the optimization. The maximum production of PBPs obtained with combinatory approach of GA-Fuzzy was 408.5 mg/L at an optimum combination of factors (FAC 0.153 g/L, K2HPO4 0.2 g/L and Trace metals 0.5 ml/L) which was a 2.13 fold more than the control medium. This novel approach is very useful for modulating biological processes since various nutrients and metabolites have greater influence on these processes.