Modeling and optimization of docosahexaenoic acid production by Schizochytrium sp. based on kinetic modeling and genetic algorithm optimized artificial neural network

• Published in: Bioresource technology Vol. 424; p. 132291
• Main Authors: Chen, Zi-Lei, Lian, Hui, Yang, Lin-Hui, Wu, Yang, Ren, Bo, Guo, Dong-Sheng
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.05.2025
• Subjects: Algorithms; Biomass; Bioprocess optimization; bioprocessing; biosynthesis; Docosahexaenoic acid; Docosahexaenoic Acids - biosynthesis; Fermentation; Fermentation kinetics; Genetic Algorithms; Kinetics; Machine learning; Models, Biological; neural networks; Neural Networks, Computer; Schizochytrium; Schizochytrium sp; Stramenopiles - metabolism; Bioprocess optimization; Schizochytrium sp; Docosahexaenoic acid; Machine learning; Fermentation kinetics
• ISSN: 0960-8524; 1873-2976; 1873-2976
• DOI: 10.1016/j.biortech.2025.132291

[Display omitted] •Kinetic models described dynamic DHA production by Schizochytrium sp.•ANN predicted fermentation results, addressing traditional model limitations.•ANN-GA achieved a high R2 of 0.988, reaching a 10.4% increase in DHA yield.•Combined modeling approach enhanced DHA production scalability. Docosahexaenoic acid (DHA), an essential ω-3 polyunsaturated fatty acid, is efficiently biosynthesized by Schizochytrium sp., yet its bioprocess optimization remains constrained by dynamic interdependencies between cultivation parameters and metabolic shifts. This study establishes a framework integrating kinetic modeling and machine learning to improve DHA production. Kinetic models based on Logistic and Luedeking-Piret equations were utilized to describe dynamic biomass, lipid and DHA production. An artificial neural network (ANN) trained on fermentation data predicted biomass and DHA yield, while genetic algorithm (GA) optimization elevated predictive accuracy (R2 = 0.988) and overcame local optimization. The ANN-GA model identified optimal three-stage control strategy, experimentally validating a 10.4 % increase in DHA yield (45.13 g/L) compared to optimal training data. By combining kinetic models and the ANN-GA model, this study provided a scalable framework for improving DHA production and reducing experimental costs.