Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity.,npj Systems Biology and Applications

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Utilizing genome-scale models to optimize nutrient supply for sustained algal growth and lipid productivity.
npj Systems Biology and Applications ( IF 3.5 ) Pub Date : 2019-09-24 , DOI: 10.1038/s41540-019-0110-7
Chien-Ting Li ₁ , Jacob Yelsky ₁ , Yiqun Chen ₁ , Cristal Zuñiga _{2,

3} , Richard Eng ₁ , Liqun Jiang _{1,

4} , Alison Shapiro ₁ , Kai-Wen Huang ₁ , Karsten Zengler _{2,

3,

5} , Michael J Betenbaugh ₁

Affiliation

Nutrient availability is critical for growth of algae and other microbes used for generating valuable biochemical products. Determining the optimal levels of nutrient supplies to cultures can eliminate feeding of excess nutrients, lowering production costs and reducing nutrient pollution into the environment. With the advent of omics and bioinformatics methods, it is now possible to construct genome-scale models that accurately describe the metabolism of microorganisms. In this study, a genome-scale model of the green alga Chlorella vulgaris (iCZ946) was applied to predict feeding of multiple nutrients, including nitrate and glucose, under both autotrophic and heterotrophic conditions. The objective function was changed from optimizing growth to instead minimizing nitrate and glucose uptake rates, enabling predictions of feed rates for these nutrients. The metabolic model control (MMC) algorithm was validated for autotrophic growth, saving 18% nitrate while sustaining algal growth. Additionally, we obtained similar growth profiles by simultaneously controlling glucose and nitrate supplies under heterotrophic conditions for both high and low levels of glucose and nitrate. Finally, the nitrate supply was controlled in order to retain protein and chlorophyll synthesis, albeit at a lower rate, under nitrogen-limiting conditions. This model-driven cultivation strategy doubled the total volumetric yield of biomass, increased fatty acid methyl ester (FAME) yield by 61%, and enhanced lutein yield nearly 3 fold compared to nitrogen starvation. This study introduces a control methodology that integrates omics data and genome-scale models in order to optimize nutrient supplies based on the metabolic state of algal cells in different nutrient environments. This approach could transform bioprocessing control into a systems biology-based paradigm suitable for a wide range of species in order to limit nutrient inputs, reduce processing costs, and optimize biomanufacturing for the next generation of desirable biotechnology products.

中文翻译：

利用基因组规模模型优化营养供应，以实现藻类的持续生长和脂质生产力。

营养物质的可用性对于藻类和其他用于产生有价值的生化产品的微生物的生长至关重要。确定培养物的最佳营养供应水平可以消除过量营养的喂养，降低生产成本并减少营养对环境的污染。随着组学和生物信息学方法的出现，现在可以构建准确描述微生物代谢的基因组规模模型。在这项研究中，绿藻小球藻 (iCZ946) 的基因组规模模型被用来预测在自养和异养条件下多种营养物质的摄食，包括硝酸盐和葡萄糖。目标函数从优化生长改为最小化硝酸盐和葡萄糖的吸收率，从而能够预测这些营养素的饲喂率。代谢模型控制 (MMC) 算法经过验证可实现自养生长，在维持藻类生长的同时节省 18% 的硝酸盐。此外，我们通过在异养条件下同时控制高水平和低水平葡萄糖和硝酸盐的葡萄糖和硝酸盐供应，获得了类似的生长曲线。最后，控制硝酸盐的供应，以保持蛋白质和叶绿素的合成，尽管在氮限制条件下合成速度较低。与氮饥饿相比，这种模型驱动的栽培策略使生物量总体积产量增加了一倍，脂肪酸甲酯 (FAME) 产量增加了 61%，叶黄素产量提高了近 3 倍。这项研究引入了一种整合组学数据和基因组规模模型的控制方法，以便根据不同营养环境中藻类细胞的代谢状态来优化营养供应。这种方法可以将生物加工控制转变为适用于多种物种的基于系统生物学的范式，以限制营养输入，降低加工成本，并优化下一代理想生物技术产品的生物制造。

更新日期：2019-09-24

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