Mathematical Modeling of Growth and Paclitaxel Biosynthesis in Corylus avellana Cell Culture Responding to Fungal Elicitors Using Multilayer Perceptron-Genetic Algorithm

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 HEF 17 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 HEF 17 , either individually or combined treatment, in mid and late log phase was processed for modeling and optimizing growth and paclitaxel biosynthesis regarding CE and CF concentration levels, elicitor adding day, and CSC harvesting time using multilayer perceptron-genetic algorithm (MLP-GA). The results displayed higher accuracy of MLP-GA models (0.89-0.95) than regression models (0.56-0.85). The great accordance between the predicted and observed values of output variables (dry weight, intracellular, extracellular and total yield of paclitaxel, and also extracellular paclitaxel portion) for both training and testing subsets supported the excellent performance of developed MLP-GA models. MLP-GA method presented a promising tool for selecting the optimal conditions for maximum paclitaxel biosynthesis. An Excel ® estimator, HCC-paclitaxel, was designed based on MLP-GA model as an easyto-use tool for predicting paclitaxel biosynthesis in C. avellana CSC responding to fungal elicitors.

Modeling of paclitaxel biosynthesis elicitation in Corylus avellana cell culture using adaptive neuro-fuzzy inference system-genetic algorithm (ANFIS-GA) and multiple regression methods

“…Nevertheless, Taxus recalcitrant behavior under in vitro culture is a drawback for fast-growing in vitro culture establishment of these valuable species [13]. Corylus avellana is a promising alternative for paclitaxel production because of its advantages including easy in vitro cultivation and fast-growing cells, and also extensive availability [13][14][15][16][17][18][19]. Large scale production of secondary metabolite (SM) through plant cell culture needs to apply several strategies including high-yielding cell line, growth medium optimization, precursor feeding, elicitation, etc.…”

“…Poor non-linear predictive and fitting abilities of traditional modeling methods [18,19,38,[42][43][44] have shifted the studies to the use of data mining techniques such as artificial neural network (ANN) and neuro-fuzzy models. These models are able to identify and learn correlated patterns between input variables and corresponding target values in a complex and nonlinear process.…”

“…Mathematical optimization has been successfully used in plant science [19,49,[51][52][53]. Genetic algorithm (GA) as a robust optimizing tool has been successfully used to optimize culture medium composition for pear rootstock proliferation [37,38], in vitro rooting of Prunus rootstock [54] and melon differentiation [55].…”

Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases

“…However, difficulty in achieving an optimized solution can be considered as one of the demerit points of most machine learning algorithms [22][23][24][25][26][27][28][29]. To overcome this bottleneck, Zhang et al [30] employed the genetic algorithm (GA) as one of the common optimization algorithms for optimizing relative humidity, light duration, agar concentration, and culture temperature in order to maximize indirect shoot organogenesis in Cucumis melo.…”

Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study

“…In vitro culture consists of highly complex and nonlinear processes such as dedifferentiation, re-differentiation, or differentiation due to the genetic and environmental factors [18][19][20][21]. Therefore, it would be difficult to predict different in vitro culture parameters such as callogenesis rate, embryogenesis rate, and the number of somatic embryos as well as optimize factors involved in these parameters by simple conventional mathematical methods [22][23][24]. Furthermore, biological processes such as somatic embryogenesis cannot be described as a simple stepwise algorithm, especially when the datasets are highly noisy and complex [25][26][27][28][29].…”