Modeling of astaxanthin biosynthesis via machine learning, mathematical and metabolic network modeling.

Natural astaxanthin is synthesized by diverse organisms including: bacteria, fungi, microalgae, and plants involving complex cellular processes, which depend on numerous interrelated parameters. Nonetheless, existing knowledge regarding astaxanthin biosynthesis and the conditions influencing astaxanthin accumulation is fairly limited. Thus, manipulation of the growth conditions to achieve desired biomass and astaxanthin yields can be a complicated process requiring cost-intensive and time-consuming experiment-based research. As a potential solution, modeling and simulation of biological systems have recently emerged, allowing researchers to predict/estimate astaxanthin production dynamics in selected organisms. Moreover, mathematical modeling techniques would enable further optimization of astaxanthin synthesis in a shorter period of time, ultimately contributing to a notable reduction in production costs. Thus, the present review comprehensively discusses existing mathematical modeling techniques which simulate the bioaccumulation of astaxanthin in diverse organisms. Associated challenges, solutions, and future perspectives are critically analyzed and presented.

Wastewater-based microalgal biorefineries for the production of astaxanthin and co-products: Current status, challenges and future perspectives.

The freshwater microalgae Haematococcus pluvialis and Chlorella zofingiensis are attractive biorefinery feedstocks in view of their ability to simultaneously synthesize astaxanthin and other valuable metabolites. Nonetheless, there are concerns regarding the sustainability of such biorefineries due to the high freshwater footprint of microalgae cultivation. The integration of wastewater as an alternative growth media is a promising approach to reduce freshwater demand. Wastewater-based cultivation enables the recovery of essential nutrients required for microalgae growth and consequently results in phycoremediation of wastewater, thus promoting the concept of a circular economy and further enhancing the sustainability of the process. In this review, recent developments in wastewater-integrated cultivation of H. pluvialis and C. zofingiensis for astaxanthin production are discussed. Furthermore, prospective strategies for overcoming the inherent challenges of wastewater-based cultivation are reviewed. Moreover, the biorefinery potential of wastewater-grown H. pluvialis and C. zofingiensis is delineated and future perspectives of wastewater-based biorefineries are outlined.

Astaxanthin accumulation in the green microalga Haematococcus pluvialis: Effect of initial phosphate concentration and stepwise/continuous light stress.

Nutrient composition and light stress significantly affect the productivity of astaxanthin in Haemotococcus pluvialis. Hence, the present study aimed to investigate the effect of initial phosphate concentration and two distinct light regimes on astaxanthin accumulation in H. pluvialis. In the green stage, microalgae were cultivated in different initial phosphate concentrations under 2000 lx and a 12:12 h photoperiod. To initiate astaxanthin accumulation, an increased light intensity of 5000 lx was provided using two methods; (i) stepwise light stress, where a 12:12 h photoperiod was provided for 14 days, followed by 14 days of continuous illumination, and (ii) continuous illumination for 28 days. Phosphate limitation and continuous light stress were favourable to enhance cellular astaxanthin accumulation, which reached 7% by weight. The highest astaxanthin concentration of 27.0 ±â€¯1.9 mg/L and lowest specific light energy consumption of 32.9 ±â€¯2.3 kW h/g astaxanthin were reported in cultures grown in 41 mg/L phosphate under continuous light stress.

Comparative assessment of pretreatment strategies for production of microalgae-based biodiesel from locally isolated Chlorella homosphaera.

The yield and quality of lipids extracted from microalgal biomass are critical factors in the production of microalgae-based biodiesel. The green microalga Chlorella homosphaera, isolated from Beira Lake, Colombo, Sri Lanka was employed in the present study to identify the effect of chlorophyll removal and cell disruption methods on lipid extraction yield, fatty acid methyl ester (FAME) profile and quality parameters of biodiesel; including cetane number (CN), iodine value (IV), degree of unsaturation (DU) and high heating value (HHV). In the first section of this study, chlorophyll was removed from dry microalgae biomass prior to lipid extraction. Through the analysis of FAME profiles, it was observed that chlorophyll removal yielded biodiesel of enhanced quality, albeit with a lipid loss of 44.2% relative to the control. In the second section of the study, mechanical cell disruption strategies including grinding, autoclaving, water bath heating and microwaving were employed to identify the most effective method to improve lipid recovery from chlorophyll-removed microalgae biomass. Autoclaving (121 °C, 20 min sterilization time, total time 2 h) was the most effective cell disruption technique of the methods tested, in terms of lipid extraction yield (39.80%) and also biodiesel quality. Moreover, it was observed that employing cell disruption subsequent to chlorophyll removal has a significant impact on the FAME profile of microalgae-based biodiesel, and consequently served to increase HHV and CN although IV and DU did not vary significantly.

Development of an artificial neural network model to simulate the growth of microalga Chlorella vulgaris incorporating the effect of micronutrients.

Artificial neural network (ANN) models can be trained to simulate the dynamic behavior of biological systems. In the present study, an ANN model was developed upon multilayer perceptron neural network architecture with 23-20-1 configuration to predict the cell concentration of microalga Chlorella vulgaris at a given time. Irradiance level, photoperiod, temperature, air flow rate, CO2 percentage of the air stream, initial cell concentration, cultivation time and the nutrient concentrations of the media were considered as the input variables of the model. Resilient backpropagation learning algorithm was used to train the model by means of 484 experimental data belonging to four studies. Bias and accuracy factors of the developed model fall into the range of 0.95-1.11 indicating the model has an excellent prediction ability. Parity plot showed a good agreement between the predicted and experimental values with R2 = 0.98. Relative importance of the inputs was evaluated using Garson's algorithm. The results of the study indicated that CO2 supply had the highest impact on the growth of C. vulgaris within the selected range of input parameters. Among macronutrients and micronutrients, highest influence was demonstrated by nitrogen and copper respectively.