Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes
Sampling the Solution Space in Genome-Scale Metabolic Networks Reveals Transcriptional Regulation in Key Enzymes
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DOI：
10.1371/journal.pcbi.1000859
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发表时间：
2010-07-01
影响因子：
4.3
通讯作者：
Nielsen, Jens
中科院分区：
文献类型：
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作者：
Bordel, Sergio;Agren, Rasmus;Nielsen, Jens
基因组规模的代谢模型可用于越来越多的生物体，并可用于定义可行的代谢通量分布的区域。在这项工作中，我们使用的约束条件的一小部分实验代谢通量，这减少了可行的代谢状态的区域。一旦可行的通量分布的区域已经被定义，一组可能的通量分布是通过随机采样获得的，并且计算基因组规模模型中的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之间每个反应速率的变化的显著性，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允许鉴定在通量变化和表达变化（转录调节）之间显示显著相关性的酶，以及通量变化可能仅由代谢物浓度的变化（代谢调节）驱动的反应。分析了酿酒酵母由于在四种不同碳源上生长和由于五种基因缺失而引起的变化。具有转录调控功能的酶在某些转录因子中富集。这在以前没有报道过。该方法所提供的信息可以指导新的代谢工程策略的发现或用于治疗代谢性疾病的药物靶点的鉴定。
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.