Biotechnology and Bioprocess Engineering 2020; 25(1): 53-61  
Comparison and Analysis of Published Genome-scale Metabolic Models of Yarrowia lipolytica
Yu Xu1, Roman Holic2, and Qiang Hua3,*
1State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai 200237, China
2Institute of Animal Biochemistry and Genetics, Centre of Biosciences, Slovak Academy of Sciences, Dubravska cesta 9, Bratislava, Slovakia
3Shanghai Collaborative Innovation Center for Biomanufacturing Technology, Shanghai 200237, China
Correspondence to: Qiang Hua*
Shanghai Collaborative Innovation Center for Biomanufacturing Technology, Shanghai 200237, China
Tel: +86-21-6425-0972; Fax: +86-21-6425-0972
Received: May 28, 2019; Revised: September 1, 2019; Accepted: September 17, 2019; Published online: February 29, 2020.
© The Korean Society for Biotechnology and Bioengineering. All rights reserved.

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background: Genome-scale metabolic models (GEMs) are powerful tools for predicting metabolic flux distributions, understanding complex cell physiology, and guiding the improvement of cell metabolism and production. Yarrowia lipolytica is known for its ability to accumulate lipids and has been widely employed to produce many important metabolites as an ideal host microorganism. There are six GEMs reconstructed for this strain by different research groups, which, however, may cause confusion for model users. It is therefore necessary to understand and analyze the existing models comprehensively. Results: Different simulation results of the published GEMs of Y. lipolytica were analyzed based on experimental data, in order to understand the differences among models and identify whether there were common problems in model construction. First, specific growth rates (µ) under various culture conditions were simulated by different models, showing that the biomass generation equation in models had significant influence on the accuracy of simulation results. In addition, simulation and analysis of intracellular flux distributions revealed several inaccurate descriptions on the reversibility of reactions involving currency metabolites in the models. Finally, specific metabolite formation rates were predicted for different target products, and large discrepancies among the different models were observed. The corresponding solutions were then proposed according to the findings of the above model problems. Conclusions: We have corrected the existing GEMs of Y. lipolytica and the prediction performances of the models have been significantly improved. Several suggestions for better construction and refinement of genome-scale metabolic network models were also provided.
Keywords: genome-scale metabolic models, Yarrowia lipolytica, intracellular flux distribution, simulation of cell growth and production

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