Banos Daniel Trejo, Trébulle Pauline, Elati Mohamed
UMR 8030 Génomique Métabolique / Laboratoire iSSB CEA-CNRS-UEVE, Genopole campus 1, 5 rue Henri Desbruères, Cedex Évry, 91030, France.
Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, Jouy-en-Josas, 78350, France.
BMC Syst Biol. 2017 Dec 21;11(Suppl 7):134. doi: 10.1186/s12918-017-0507-0.
Genome-scale metabolic models provide an opportunity for rational approaches to studies of the different reactions taking place inside the cell. The integration of these models with gene regulatory networks is a hot topic in systems biology. The methods developed to date focus mostly on resolving the metabolic elements and use fairly straightforward approaches to assess the impact of genome expression on the metabolic phenotype.
We present here a method for integrating the reverse engineering of gene regulatory networks into these metabolic models. We applied our method to a high-dimensional gene expression data set to infer a background gene regulatory network. We then compared the resulting phenotype simulations with those obtained by other relevant methods.
Our method outperformed the other approaches tested and was more robust to noise. We also illustrate the utility of this method for studies of a complex biological phenomenon, the diauxic shift in yeast.
基因组规模代谢模型为合理研究细胞内发生的不同反应提供了契机。这些模型与基因调控网络的整合是系统生物学中的一个热门话题。迄今为止开发的方法主要集中于解析代谢元件，并使用相当直接的方法来评估基因组表达对代谢表型的影响。
我们在此提出一种将基因调控网络的逆向工程整合到这些代谢模型中的方法。我们将我们的方法应用于一个高维基因表达数据集，以推断一个背景基因调控网络。然后，我们将所得的表型模拟结果与通过其他相关方法获得的结果进行比较。
我们的方法优于所测试的其他方法，并且对噪声更具鲁棒性。我们还说明了该方法在研究一种复杂生物学现象——酵母中的二次生长转变方面的实用性。
