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Modeling regulatory networks using machine learning for systems metabolic engineering.
Current Opinion in Biotechnology ( IF 7.7 ) Pub Date : 2020-04-14 , DOI: 10.1016/j.copbio.2020.02.014
Mun Su Kwon 1 , Byung Tae Lee 1 , Sang Yup Lee 2 , Hyun Uk Kim 3
Affiliation  

Systems metabolic engineering attempts to engineer a production host's biological network to overproduce valuable chemicals and materials in a sustainable manner. In contrast to genome-scale metabolic models that are well established, regulatory network models have not been sufficiently considered in systems metabolic engineering despite their importance and recent notable advances. In this paper, recent studies on inferring and characterizing regulatory networks at both transcriptional and translational levels are reviewed. The recent studies discussed herein suggest that their corresponding computational methods and models can be effectively applied to optimize a production host's regulatory networks for the enhanced biological production. For the successful application of regulatory network models, datasets on biological sequence-phenotype relationship need to be more generated.

中文翻译:

使用机器学习为系统代谢工程建模监管网络。

系统代谢工程学试图对生产宿主的生物网络进行工程设计,以可持续方式过量生产有价值的化学物质和材料。与已建立的基因组规模的代谢模型相反,尽管系统重要性,但是最近的重大进展,尚未在系统代谢工程中充分考虑调节网络模型。在本文中,综述了有关在转录和翻译水平上推断和表征调控网络的最新研究。本文讨论的最新研究表明,它们相应的计算方法和模型可以有效地用于优化生产宿主的调控网络,以提高生物产量。为了成功应用监管网络模型,
更新日期:2020-04-20
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