Optimization to the Culture Conditions forPhellinusProduction with Regression Analysis and Gene-Set Based Genetic Algorithm

Abstract

Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization.

Optimization of culture conditions for penicilazaphilone C production by a marine-derived fungus Penicillium sclerotiorum M-22

“…Fermentation time, initial pH and concentration of nutritional ingredients play significant roles in the fungal fermentation, as these have been reported to influence production of natural products by other micro-organisms (El-Naggar Nel et al 2015; Li et al 2016). In this work, we found fermentation time, initial pH, corn meal concentration, yeast extract concentration and crude sea salt concentration were five significant factors for PAC production from the above experiment.…”

“…; Li et al . ). In this work, we found fermentation time, initial pH, corn meal concentration, yeast extract concentration and crude sea salt concentration were five significant factors for PAC production from the above experiment.…”

Optimization to the Phellinus experimental environment based on classification forecasting method

“…In our previous paper in [ 27 ], we use the data collected during these experiments and take the statistical methods to establish a mathematical model in order to forecast the Flavonoid yield. Flavonoid yield is the most important product of Phellinus.…”

A novel method for multifactorial bio-chemical experiments design based on combinational design theory

“…The general idea is to learn from the experimental data, and then achieving a prediction model of the experiment, which matches the known data in a acceptable level. With the model, some optimal conditions for maximizing the outcome or minimizing the cost can be obtained [ 23 – 26 ]. However, the experimental data obtained or collected by classical experimental design methods, such as Plackett-Burman designs, response surface methodology, central composite design, the data is quite unbalanced, are not balanced well, such that we cannot get well fitting models by using machine learning methods.…”