Optimizing Xylanase Production: Bridging Statistical Design and Machine Learning for Improved Protein Production - Merve Aslı Ergün; Başak Esin Köktürk-Güzel; Tuğba Keskin-Gündoğdu (Fermentation, 2025)
Proteins are crucial for medicine, pharmaceuticals, food, and environmental applications since they are used in various fields such as synthesis of drugs, industrial enzyme production, biodegradable plastics, bioremediation processes, etc. Xylanase is an important and versatile enzyme with applications across various industries, including pulp and paper, biofuel production, food processing, textiles, laundry detergents, and animal feed. Key parameters in biotechnological protein production include temperature, pH, and working volumes and especially medium compositions where optimization is crucial for large-scale applications due to cost considerations. Machine learning methods have emerged as effective alternatives to traditional statistical approaches in optimization. This study focuses on optimizing xylanase production via bioprocesses by employing regression analysis on datasets from various studies. The extreme gradient boosting (XGBoost) regression model was applied to predict xylanase activity under different experimental conditions, accurately predicting xylanase activity and identifying the significance of each variable. This study utilized experimentally derived datasets from peer-reviewed publications, in which the corresponding laboratory experiments had already been conducted and validated. The results demonstrate that machine learning methods can effectively optimize protein production processes, offering a strong alternative to traditional statistical approaches.