arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1701.01744q-bio.MN
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代谢模式揭示了对遗传扰动的调节反应

Metabolite patterns reveal regulatory responses to genetic perturbations

Tolutola Oyetunde, Jeffrey Czajka, Gang Wu, Cynthia Lo, Yinjie Tang

摘要:

遗传和环境扰动实验已被用于研究微生物,以深入了解转录调控,适应性进化和其他细胞动力学。这些研究有可能使合理的应变设计。不幸的是,由于不同的实验条件和方法,实验确定的细胞内流量分布往往是不一致的或不可比较的。计算菌株设计依赖于基于约束的重建和分析(COBRA)技术来预测基因敲除的影响,例如通量平衡分析(FBA)、调节开/关最小化(ROOM)、代谢调节最小化(MOMA)、代谢网络中的相对最优性(RIPATCH)。这些敲除预测方法中的大多数是基于保存被认为代表细胞调控结构的固有通量模式(野生型和突变体之间)。然而,最近已经证明,这些方法与实验数据显示出较差的一致性。为了提高敲除预测和随后的计算菌株设计的保真度,我们开发了REMEP,一种以代谢物为中心的方法。我们通过比较不同方法对E. coli和S.在分批培养中生长的酿酒酵母。REMEP保留了早期算法的大部分功能,但在捕获细胞对遗传扰动的反应方面要准确得多。其主要原因是REMEP依赖于细胞调节结构在代谢物模式上留下签名而不仅仅是通量模式的假设。REMEP也将被证明有助于揭示细胞调控的新见解。

英文摘要:

Genetic and environmental perturbation experiments have been used to study microbes in a bid to gain insight into transcriptional regulation, adaptive evolution, and other cellular dynamics. These studies have potential in enabling rational strain design. Unfortunately, experimentally determined intracellular flux distribution are often inconsistent or incomparable due to different experimental conditions and methodologies. Computational strain design relies on constraint-based reconstruction and analysis (COBRA) techniques to predict the effect of gene knockouts such as flux balance analysis (FBA), regulatory on/off minimization(ROOM), minimization of metabolic adjustment (MOMA), relative optimality in metabolic networks (RELATCH). Most of these knock-out prediction methods are based on conserving inherent flux patterns (between wild type and mutant) that are thought to be representative of the cellular regulatory structure. However, it has been recently demonstrated that these methods show poor agreement with experimental data. To improve the fidelity of knockout predictions and subsequent computational strain design, we developed REMEP, a metabolite-centric method. We demonstrate the improved performance of REMEP by comparing the different methods on experimental knockout data of E. coli, and S. cerevisiae grown in batch cultures. REMEP retains most of the features of earlier algorithms but is much more accurate in capturing cellular responses to genetic perturbations. A primary reason for this is that REMEP relies on the assumption that cellular regulatory structure leaves a signature on metabolite patterns and not just flux patterns. REMEP will also prove useful in uncovering novel insights into cellular regulation and control.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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