Kinetic modeling of growth and lipid body induction in Chlorella pyrenoidosa under heterotrophic conditions. (October 2016)

• Record Type: Journal Article
• Authors: Sachdeva, Neha; Kumar, G. Dinesh; Gupta, Ravi Prakash; Mathur, Anshu Shankar; Manikandan, B.; Basu, Biswajit; Tuli, Deepak Kumar
• Is Part Of: Bioresource technology. Volume 218(2016)
• Page Start: 934
• Page End: 943
• Publication Date: 2016-10
• Subjects: Heterotrophic microalgae -- Chlorella pyrenoidosa -- MATLAB -- Kinetic modeling -- Lipid accumulation -- Nutrient stress; 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.2016.07.063
• Languages: English
• ISSNs: 0960-8524

Abstract

Highlights: A heterotrophic mechanistic model describing biomass and lipid productivity of Chlorella pyrenoidosa NCIM 2738. Model simulated under non-limiting carbon and limiting nitrogen concentration. Growth and nutrient stress modes used for the prediction of endogenous lipid synthesis. MATLAB and Genetic Algorithm were employed for parameter prediction and validation. Flexible model capable of predicting lipid synthesis under other stress conditions. Abstract: The aim of the present work was to develop a mathematical model to describe the biomass and (total) lipid productivity of Chlorella pyrenoidosa NCIM 2738 under heterotrophic conditions. Biomass growth rate was predicted by Droop's cell quota model, while changes observed in cell quota (utilization) under carbon excess conditions were used for the modeling and predicting the lipid accumulation rate. The model was simulated under non-limiting (excess) carbon and limiting nitrate concentration and validated with experimental data for the culture grown in batch (flask) mode under different nitrate concentrations. The present model incorporated two modes (growth and stressed) for the prediction of endogenous lipid synthesis/induction and aimed to predict the effect and response of the microalgae under nutrient starvation (stressed) conditions. MATLAB and Genetic Algorithm were employed for the prediction and validation of the model parameters.