ArticleBMC biotechnology2025
Artificial Neural Network - Multi-Objective Genetic Algorithm based optimization for the enhanced pigment accumulation in Synechocystis sp. PCC 6803.
Article in BMC biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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Abstract
backgroundNatural colorants produced by the cyanobacterium include carotenoids, chlorophyll a and phycocyanin. The current study used the Synechocystis sp. PCC 6803 to examine how abiotic stress conditions, such as low temperature as well as high light intensity, affect the pigment accumulations in comparison to the control conditions. Additionally, using the response surface methodology (RSM) and artificial neural network - multi-objective genetic algorithm (ANN-MOGA), the impact of several nitrogen sources such as urea, ammonium chloride, and sodium nitrate as nutritional stress on the pigment accumulations in the Synechocystis sp. PCC 6803 was examined.
resultsThe results showed that the pigment accumulation was more pronounced when urea and ammonium chloride was used in combination with nitrate, respectively, as nitrogen source. With the help of our prediction model that used ANN-MOGA, we were able to enhance the synthesis of chlorophyll a, carotenoids, and phycocyanin by 21.93 µg/mL, 9.78 µg/mL, and 0.05 µg/mL, respectively compared to control with 6.37, 3.88 and 0.008 µg/mL. The significant scavenging activity of pigment was showed with 7.66 ± 0.001 values of IC50. Additionally, a very good correlation of coefficient (R
conclusionsThe findings lays the groundwork for future attempts to turn cyanobacteria into a commercially viable source of natural pigments by demonstrating the benefits of using the RSM and machine learning techniques like ANN-MOGA to optimise the production of cyanobacterial pigments. The significant scavenging and antioxidant activities like CAT, GPX and APX were also shown by the pigments of the Synechocystis sp. PCC 6803. Furthermore, these machine learning tools can be used as a model to improve and optimize the yields for other metabolites production.
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