Kavšček2015 - Genome-scale metabolic model of Yarrowia lipolytica (iMK735)

Kavšček2015 - Genome-scale
metabolic model of Yarrowia lipolytica (iMK735)

This model is described in the article:

Optimization of lipid
production with a genome-scale model of Yarrowia
lipolytica.

Kavšček M, Bhutada G, Madl
T, Natter K.
BMC Syst Biol 2015; 9: 72
Abstract:

Yarrowia lipolytica is a non-conventional yeast that is
extensively investigated for its ability to excrete citrate or
to accumulate large amounts of storage lipids, which is of
great significance for single cell oil production. Both traits
are thus of interest for basic research as well as for
biotechnological applications but they typically occur
simultaneously thus lowering the respective yields. Therefore,
engineering of strains with high lipid content relies on novel
concepts such as computational simulation to better understand
the two competing processes and to eliminate citrate
excretion.Using a genome-scale model (GSM) of baker's yeast as
a scaffold, we reconstructed the metabolic network of Y.
lipolytica and optimized it for use in flux balance analysis
(FBA), with the aim to simulate growth and lipid production
phases of this yeast. We validated our model and found the
predictions of the growth behavior of Y. lipolytica in
excellent agreement with experimental data. Based on these
data, we successfully designed a fed-batch strategy to avoid
citrate excretion during the lipid production phase. Further
analysis of the network suggested that the oxygen demand of Y.
lipolytica is reduced upon induction of lipid synthesis.
According to this finding we hypothesized that a reduced
aeration rate might induce lipid accumulation. This prediction
was indeed confirmed experimentally. In a fermentation
combining these two strategies lipid content of the biomass was
increased by 80%, and lipid yield was improved more than
four-fold, compared to standard conditions.Genome scale network
reconstructions provide a powerful tool to predict the effects
of genetic modifications and the metabolic response to
environmental conditions. The high accuracy and the predictive
value of a newly reconstructed GSM of Y. lipolytica to optimize
growth conditions for lipid accumulation are demonstrated.
Based on these findings, further strategies for engineering Y.
lipolytica towards higher efficiency in single cell oil
production are discussed.

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Kavšček2015——解脂耶氏酵母（Yarrowia lipolytica）基因组规模代谢模型（iMK735）

本模型对应的研究文章为：
《基于解脂耶氏酵母基因组规模模型优化脂质生产》
作者：Kavšček M、Bhutada G、Madl T、Natter K，发表于《BMC系统生物学（BMC Syst Biol）》2015年；9：72。

摘要：
解脂耶氏酵母是一种非常规酵母，因其可分泌柠檬酸或大量积累储存脂质而被广泛研究，该特性在单细胞油脂生产中具有重要应用价值。上述两种代谢性状不仅在基础研究中具备探索意义，同时也具有生物技术应用潜力，但二者通常会同时发生，进而降低各自的产物得率。因此，构建高脂质含量的工程菌株需要借助计算模拟等新型技术手段，以更好地解析这两种竞争代谢过程，并阻断柠檬酸分泌途径。
本研究以酿酒酵母基因组规模模型（genome-scale model, GSM）作为支架，重构了解脂耶氏酵母的代谢网络，并对其进行优化以用于通量平衡分析（flux balance analysis, FBA），目标是模拟该酵母的生长与脂质生产阶段。我们对所构建的模型进行了验证，发现其对解脂耶氏酵母生长行为的预测结果与实验数据高度吻合。基于上述结果，我们成功设计了补料分批（fed-batch）培养策略，可在脂质生产阶段避免柠檬酸的分泌。进一步的网络分析表明，解脂耶氏酵母在脂质合成被诱导后，其需氧量会出现降低。基于这一发现，我们提出假设：降低通气速率可促进脂质积累，该预测随后通过实验得到了证实。与标准培养条件相比，结合上述两种策略的发酵工艺可使生物质的脂质含量提升80%，脂质得率提高四倍以上。
基因组规模网络重构为预测遗传修饰的影响以及代谢过程对环境条件的响应提供了强有力的工具。本研究验证了新构建的解脂耶氏酵母基因组规模模型的高精度与预测价值，可用于优化脂质积累的培养条件。基于上述发现，本文还讨论了进一步工程改造解脂耶氏酵母以提升单细胞油脂生产效率的相关策略。

本模型存储于生物模型数据库（BioModels Database），编号为MODEL1510060001。
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