Mathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm
Mina Salehi, Siamak Farhadi, Ahmad Moieni, Naser Safaie, Hamed Ahmadi
Abstract
Paclitaxel is the top-selling anticancer medicine in the world. In vitro culture of Corylus avellana has been made known as a promising and inexpensive strategy for producing paclitaxel. Fungal elicitors have been named as the most efficient strategy for enhancing the biosynthesis of secondary metabolites in plant cell culture. In this study, endophytic fungal strain HEF17 was isolated from C. avellana, and identified as Camarosporomyces flavigenus. C. avellana cell suspension culture (CSC) elicited with cell extract (CE) and culture filtrate (CF) derived from strain HEF17, either individually or combined treatment, in mid and late log phase were processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, fungal elicitor adding day and harvesting day of cell culture using artificial neural network-genetic algorithm (ANN-GA). The results displayed the higher accuracy of ANN models (0.89-0.95) than regression models (0.56-0.85). A great accordance between the predicted and observed values of DW, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion for both training and validation subsets support excellent performance of developed ANN-GA models. ANN-GA method presented a promising tool for selecting optimal conditions for maximum paclitaxel biosynthesis. An Excel® calculator, HCC-paclitaxel, was designed based on ANN-GA model as an easy-to-use tool for predicting total yield of paclitaxel in C. avellana cell culture responding to fungal elicitors.