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首页> 外文期刊>ACS Synthetic Biology >Tn-Core: A Toolbox for Integrating Tn-seq Gene Essentiality Data and Constraint-Based Metabolic Modeling
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Tn-Core: A Toolbox for Integrating Tn-seq Gene Essentiality Data and Constraint-Based Metabolic Modeling

机译:TN-Core:一种用于集成TN-SEQ基因基本度数据和基于约束的代谢建模的工具箱

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The design of synthetic cells requires a detailed understanding of the relevance of genes and gene networks underlying complex cellular phenotypes. Transposon-sequencing (Tn-seq) and constraint-based metabolic modeling can be used to probe the core genetic and metabolic networks underlying a biological process. Integrating these highly complementary experimental and in silico approaches has the potential to yield a highly comprehensive understanding of the core networks of a cell. Specifically, it can facilitate the interpretation of Tn-seq data sets and identify gaps in the data that could hinder the engineering of the cellular system, while also providing providing refined models for the accurate predictions of cellular metabolism. Here, we present Tn-Core, the first easy-to-use computational pipeline specifically designed for integrating Tn-seq data with metabolic modeling, prepared for use by both experimental and computational biologists. Tn-Core is a MATLAB toolbox that contains several custom functions, and it is built upon existing functions within the COBRA Toolbox and the TIGER Toolbox. Tn-Core takes as input a genome-scale metabolic model, Tn-seq data, and optionally RNA-seq data, and returns: (i) a context-specific core metabolic model; (ii) an evaluation of redundancies within core metabolic pathways, and optionally (iii) a refined genome-scale metabolic model. A simple, user-friendly workflow, requiring limited knowledge of metabolic modeling, is provided that allows users to run the analyses and export the data as easy-to-explore files of value to both experimental and computational biologists. We demonstrate the utility of Tn-Core using Sinorhizobium meliloti, Pseudomonas aeruginosa, and Rhodobacter sphaeroides genome-scale metabolic reconstructions as case studies.
机译:合成细胞的设计需要详细了解基因和基因网络潜在的复杂细胞表型的相关性。转座子测序(TN-SEQ)和基于约束的代谢建模可用于探测生物过程的核心遗传和代谢网络。整合这些高度互补的实验和硅化方法有可能对细胞的核心网络产生高度全面的了解。具体地,它可以促进TN-SEQ数据集的解释,并识别能够阻碍蜂窝系统工程的数据中的间隙,同时还提供提供精确预测细胞代谢的精确模型。在这里,我们呈现TN-Core,专门设计用于将TN-SEQ数据与代谢建模集成的第一易用的计算管道,准备用于使用实验和计算生物学家。 TN-Core是一个包含多个自定义功能的MATLAB工具箱,它建立在COBRA工具箱和TIGET工具箱中的现有功能。 TN核心以基因组级代谢模型,TN-SEQ数据和可选的RNA-SEQ数据作为输入,并返回:(i)特定于上下文的核心代谢模型; (ii)评估核心代谢途径内的冗余,以及任选的(iii)一种精制的基因组级代谢模型。提供了一种简单的用户友好的工作流程,需要有限的代谢建模知识,允许用户运行分析并将数据导出为实验和计算生物学家的易于探索值。我们展示了使用Sinorhizobium Meliloti,假单胞菌铜绿假单胞菌和乳头杆菌基因组级代谢重建作为案例研究的TN-Core的效用。

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