Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae
Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae
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DOI：
10.1101/gr.4083206
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发表时间：
2006-05-01
期刊：
影响因子：
7
通讯作者：
Palsson, BO
中科院分区：
文献类型：
--
作者：
Herrgård, MJ;Lee, BS;Palsson, BO
我们描述了使用模型驱动的分析相关的代谢转录调控的多种数据类型，发现新的调控机制，在酿酒酵母。我们已经重建了营养控制的转录调控网络控制代谢的S。酿酒酵母由55个转录因子调节750个代谢基因组成，基于主要文献中的信息。这种重建的调控网络与现有的基因组规模的代谢网络模型相结合，允许在硅片上预测调控基因缺失的生长表型以及基因表达谱。我们比较了模型预测的基因表达变化的遗传和环境扰动的实验数据，以确定潜在的新的转录因子的目标。然后，我们确定了调节级联连接转录因子的潜在目标，通过一个系统的模型扩展策略，使用已发表的全基因组染色质免疫沉淀和结合位点基序数据集。最后，我们展示了一个整合的代谢和调控网络模型预测转录因子敲除菌株生长表型的能力。这些研究说明了模型驱动的数据集成系统地发现真核细胞中调控和代谢网络中的新组件和相互作用的潜力。
We describe the use of model-driven analysis of multiple data types relevant to transcriptional regulation of metabolism to discover novel regulatory mechanisms in Saccharomyces cerevisiae. We have reconstructed the nutrient-controlled transcriptional regulatory network controlling metabolism in S. cerevisiae consisting of 55 transcription factors regulating 750 metabolic genes, based on information in the primary literature. This reconstructed regulatory network coupled with an existing genome-scale metabolic network model allows in silico prediction of growth phenotypes of regulatory gene deletions as well as gene expression profiles. We compared model predictions of gene expression changes in response to genetic and environmental perturbations to experimental data to identify potential novel targets for transcription factors. We then identified regulatory cascades connecting transcription factors to the potential targets through a systematic model expansion strategy using published genome-wide chromatin immunoprecipitation and binding-site-motif data sets. Finally, we show the ability of an integrated metabolic and regulatory network model to predict growth phenotypes of transcription factor knockout strains. These studies illustrate the potential of model-driven data integration to systematically discover novel components and interactions in regulatory and metabolic networks in eukaryotic cells.