Localization of signal transduction components in the bacterial cell

这个项目的目的是开发一个综合的理解代谢和遗传网络，控制从生长到葡萄糖饥饿的模式细菌，枯草芽孢杆菌的过渡。除了作为一种模式系统之外，枯草芽孢杆菌还是“白色生物技术”的工业主力，因为它是技术酶和其他产品（例如维生素、抗生素、增味剂和生化物质）的主要生产者。从生长到生长限制的转变是一种基本的生态生理反应，学术界将其作为环境信号处理和整合的模型进行了研究。了解这种转变对芽孢杆菌的工业发酵也至关重要，因为芽孢杆菌主要发生在营养限制下。我们的方法是将生物学数据与调节从生长到饥饿过渡的网络的数学模型相结合。该方法首先在标准化生长条件下对确定的遗传和环境扰动进行定量监测。这些数据用于调节过程的数学建模。随着项目的发展，我们在理解上的差距将被描述生物数据的结构、全基因组网络分析的失败所揭示。我们的理念是在明确定义的（子）项目中不断探索模型和数据的一致性，每个项目都涉及一个实验和一个建模合作伙伴。关键要素是模型驱动的实验设计，通过对关键变量的目标测量来测试基于模型的假设。通过标准化的命名法、模型格式和定义的输入/输出信号，模块化数学模型被集成到一致的系统表示中。总之，该项目将提供令人信服的证据，证明实验和计算科学家在一个非常先进的模式生物上的密切互动，可以显著提高我们对微生物中高度动态和复杂的调节过程的定量理解，并最终提高我们的控制能力。

The objective of this project is to develop an integrated understanding of the metabolic and genetic network that controls the transition from growth to glucose starvation in the model bacterium, Bacillus subtilis. In addition to serving as a model system, B. subtilis is an industrial workhorse for 'white biotechnology' since it serves is a primary producer of technical enzymes and other products (e.g. vitamins, antibiotics, flavour enhancers and biochemicals). The transition from growth to growth limitation is a fundamental ecophysiological response and is studied by academic researchers as a model for environmental signal processing and integration. Understanding this transition is also pivotal for industrial fermentations of Bacillus that occur predominantly under nutrient limitation. Our approach is to integrate biological data with mathematical models of the networks that regulate the transition from growth to starvation. The approach starts with quantitative monitoring of defined genetic and environmental perturbations under standardized growth conditions. These data are used for mathematical modelling of regulatory processes. As the programme develops, gaps in our understanding will be revealed by the failure of structural, genome-wide network analyses to describe the biological data. Our concept is to continuously probe model and data consistency in clearly defined (sub)projects, each involving an experimental and a modelling partner. The pivotal element is a model-driven experimental design, where model-based hypotheses are tested through targeted measurements of critical variables. Facilitated through standardized nomenclature, model formats, and defined input/output signals, modular mathematical models are then integrated into a consistent systems representation. In summary, the project will provide convincing evidence that close interactions between experimental and computational scientists on a well advanced model organism can significantly advance our quantitative understanding of, and eventually our ability to control, the highly dynamic and complex regulatory processes in microbes.