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对基因组规模代谢网络中的解决方案空间进行采样,揭示了关键酶的转录调控。

Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes.

机构信息

Systems Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.

出版信息

PLoS Comput Biol. 2010 Jul 15;6(7):e1000859. doi: 10.1371/journal.pcbi.1000859.

DOI:10.1371/journal.pcbi.1000859
PMID:20657658
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2904763/
Abstract

Genome-scale metabolic models are available for an increasing number of organisms and can be used to define the region of feasible metabolic flux distributions. In this work we use as constraints a small set of experimental metabolic fluxes, which reduces the region of feasible metabolic states. Once the region of feasible flux distributions has been defined, a set of possible flux distributions is obtained by random sampling and the averages and standard deviations for each of the metabolic fluxes in the genome-scale model are calculated. These values allow estimation of the significance of change for each reaction rate between different conditions and comparison of it with the significance of change in gene transcription for the corresponding enzymes. The comparison of flux change and gene expression allows identification of enzymes showing a significant correlation between flux change and expression change (transcriptional regulation) as well as reactions whose flux change is likely to be driven only by changes in the metabolite concentrations (metabolic regulation). The changes due to growth on four different carbon sources and as a consequence of five gene deletions were analyzed for Saccharomyces cerevisiae. The enzymes with transcriptional regulation showed enrichment in certain transcription factors. This has not been previously reported. The information provided by the presented method could guide the discovery of new metabolic engineering strategies or the identification of drug targets for treatment of metabolic diseases.

摘要

越来越多的生物体都有可用的基因组规模代谢模型,可用于定义可行代谢通量分布的区域。在这项工作中,我们使用了一小部分实验代谢通量作为约束条件,这减少了可行代谢状态的区域。一旦定义了可行通量分布的区域,就可以通过随机抽样获得一组可能的通量分布,并计算基因组规模模型中每个代谢通量的平均值和标准偏差。这些值允许估计不同条件下每个反应速率变化的显著性,并将其与相应酶的基因转录变化的显著性进行比较。通量变化和基因表达的比较可以识别出通量变化和表达变化之间存在显著相关性的酶(转录调节),以及通量变化可能仅由代谢物浓度变化驱动的反应(代谢调节)。对酿酒酵母在四种不同碳源上的生长和由于五个基因缺失而产生的变化进行了分析。具有转录调节的酶在某些转录因子中富集。这在以前的报告中没有提到过。所提供方法的信息可以指导发现新的代谢工程策略或确定用于治疗代谢疾病的药物靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/e6e1f38f72f7/pcbi.1000859.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/8b1e4f3e944f/pcbi.1000859.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/0aa423ba9159/pcbi.1000859.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/802d4c8ecfb4/pcbi.1000859.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/1a9d7cbd399e/pcbi.1000859.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/2e85eb114071/pcbi.1000859.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/e6e1f38f72f7/pcbi.1000859.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/8b1e4f3e944f/pcbi.1000859.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/0aa423ba9159/pcbi.1000859.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/802d4c8ecfb4/pcbi.1000859.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/1a9d7cbd399e/pcbi.1000859.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/2e85eb114071/pcbi.1000859.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cae/2904763/e6e1f38f72f7/pcbi.1000859.g006.jpg

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