Analysis of lipid production from Yarrowia lipolytica for renewable fuel production by machine learning

Abstract

In this work, biomass and lipid productivities of Yarrowia lipolytica were analyzed using machine learning techniques. A dataset containing 356 instances was constructed from the experimental results reported in 22 publications. The dataset was analyzed using decision trees to identify the features (descriptors) that lead to high biomass production, lipid content and lipid production. C/N ratio and fermentation time were found to be the most influential features for biomass production while the use of glucose and medium pH seemed to be more important for high lipid content. For the lipid production case, five generalizable paths leading to high values of this output were identified. One of those paths required pH to be < 6.3, high glucose and (NH4)(2)SO4 concentrations, lower concentration for yeast extract and the yeast strain not be H-222. Another one needed a pH greater than 6.3, a C/N ratio smaller than 75, a time greater than 14 h, and a strain other than W29. The same dataset was also explored deeper using association rule mining to determine the effects of individual features on output variables. It was then concluded that machine learning methods are very useful in determining the optimal conditions of biomass growth and lipid yield for Yarrowia lipolytica to produce renewable biofuels.