Quantitative Studies of Metabolic Switches in enteric bacteria

项目摘要 从分子部分和分子的知识中获得对细胞行为的定量的、预测性的理解 相互作用是系统生物学面临的最大挑战之一。在之前的赠款期间，我们建立了一个动态 预测模型细菌中蛋白质组动态的模型，以响应环境变化 条件。在下一个授权期，我们建议将这项工作扩展到预测转录组的动态。这 是一项比预测蛋白质组动态更具挑战性的任务，因为与蛋白质组不同，即使是稳定的- 转录组的状态特征还没有在数量水平上被理解；特别是 人们对转录组和蛋白质组知之甚少。我们的初步数据确定了一个以前未知的全球 作为缺失环节的大肠杆菌转录调控。我们建议从数量上建立这种全球监管效应 在不同的生长条件下，并阐明这种调控的分子机制和策略。我们会 验证并利用由该全球 在转录调控与细胞mRNA和蛋白质水平之间建立定量联系的调控 在大肠杆菌中有许多基因。通过将转录调控知识融入蛋白质组动力学模型 到目前为止，我们将建立一个框架来预测转录组在生长过程中的动态 过渡。 这项研究的实验部分涉及到现代经济学方法论和经典方法的结合 生化分析。具体而言，将针对广泛的生长条件(各种类型)收集RNA-seq数据 营养限制、抗生素治疗、瞬时转移)，以及具有不同遗传背景的菌株，包括 可滴定的突变体。总信使核糖核酸的绝对测定将进一步补充rna-seq数据。 丰度和通量，以便跨条件进行比较。然后，数据将与量化 我们已经在相同的生长条件下收集了蛋白质组和代谢组数据，这样它们就可以相互关联 对细胞生理学的影响，使定量分析和建模成为可能。后者将结合独特的体验 可在PI的实验室获得，包括对转录和转录后调控的详细定量建模 一方面是特定的基因和mRNA，另一方面是基因组规模动态的粗粒度建模。

Project Summary Attaining quantitative, predictive understanding of cellular behaviors from the knowledge of molecular parts and interactions is one of the foremost challenges of systems biology. In the previous grant period, we established a kinetic model to predict the proteome dynamics in the model bacterium E. coli, in response to changing environmental conditions. In this next grant period, we propose to extend this work to predicting the dynamics of the transcriptome. This is a much more challenging task than predicting the proteome dynamics, because unlike the proteome, even the steady- state characteristics of the transcriptome have not been understood at a quantitative level; in particular the link between the transcriptome and proteome is poorly understood. Our preliminary data identified a previously unknown global transcriptional regulation in E. coli as the missing link. We propose to establish this global regulatory effect quantitatively in different growth conditions, and to elucidate the molecular mechanism and strategy underlying this regulation. We will validate and exploit the predicted coordination between transcriptional and translational capacities provided by this global regulation to establish quantitative links between transcriptional regulation and cellular mRNA and protein levels for many genes in E. coli. By incorporating the knowledge on transcriptional regulation into the kinetic model of proteome dynamics developed so far, we will establish a framework to predict the dynamics of the transcriptome during growth transitions. Experimental components of this research involve a combination of modern ‘omic methodologies and classical biochemical analysis. Specifically, RNA-seq data will be collected for a broad range of growth conditions (various types of nutrient limitations, antibiotic treatment, transient shifts) and for strains with different genetic backgrounds including titratable mutants. The RNA-seq data will be further complemented by the absolute determination of total mRNA abundances and fluxes to enable comparison across conditions. The data will then be integrated with quantitative proteomic and metabolomic data we have already collected across the same growth conditions, so that they can be related to cellular physiology and enable quantitative analysis and model building. The latter will combine the unique experiences available at the PI’s lab, involving detailed quantitative modeling of transcriptional and post-transcriptional regulation for specific genes and mRNAs on the one hand, and coarse-grained modeling of genome-scale dynamics on the other hand.