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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data
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Reframed Genome-Scale Metabolic Model to Facilitate Genetic Design and Integration with Expression Data

机译：重组基因组规模的代谢模型，以方便基因设计和表达数据整合。

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Genome-scale metabolic network models (GEMs) have played important roles in the design of genetically engineered strains and helped biologists to decipher metabolism. However, due to the complex gene-reaction relationships that exist in model systems, most algorithms have limited capabilities with respect to directly predicting accurate genetic design for metabolic engineering. In particular, methods that predict reaction knockout strategies leading to overproduction are often impractical in terms of gene manipulations. Recently, we proposed a method named logical transformation of model (LTM) to simplify the gene-reaction associations by introducing intermediate pseudo reactions, which makes it possible to generate genetic design. Here, we propose an alternative method to relieve researchers from deciphering complex gene-reactions by adding pseudo gene controlling reactions. In comparison to LTM, this new method introduces fewer pseudo reactions and generates a much smaller model system named as gModel. We showed that gModel allows two seldom reported applications: identification of minimal genomes and design of minimal cell factories within a modified OptKnock framework. In addition, gModel could be used to integrate expression data directly and improve the performance of the E-Fmin method for predicting fluxes. In conclusion, the model transformation procedure will facilitate genetic research based on GEMs, extending their applications.
机译：基因组规模的代谢网络模型（GEM）在基因工程菌株的设计中发挥了重要作用，并帮助生物学家破译了新陈代谢。但是，由于模型系统中存在复杂的基因反应关系，因此大多数算法在直接预测代谢工程的准确基因设计方面功能有限。特别地，就基因操纵而言，预测导致过度生产的反应敲除策略的方法通常是不切实际的。最近，我们提出了一种名为逻辑模型转换（LTM）的方法，通过引入中间的伪反应来简化基因反应关联，从而有可能进行基因设计。在这里，我们提出了一种替代方法，可通过添加伪基因控制反应来减轻研究人员对复杂基因反应的解读。与LTM相比，此新方法引入了较少的伪反应，并生成了更小的模型系统，称为gModel。我们显示gModel允许很少报道的两种应用程序：在改良的OptKnock框架内识别最小基因组和设计最小细胞工厂。此外，gModel可用于直接整合表达数据并提高E-Fmin方法预测通量的性能。总之，模型转换程序将促进基于GEM的遗传研究，扩展其应用。

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