Development of a Systems Biology for Bordetella pertussis Metabolism

微生物代谢由细菌用来将营养物质转化为构成细胞的分子并为细胞过程释放能量的反应网络组成。这样的网络具有巨大的潜力，可以生产大量可能有用的分子，例如生物燃料或生物制药，但这些分子很少以高水平生产。基因组测序揭示了产生这些分子的代谢途径，这些途径的遗传修饰使我们能够出于生物技术目的操纵微生物。然而，细菌代谢的高度复杂性大大限制了这种努力。这需要新的方法，可以结合代谢系统的复杂性，并预测适当的修改。我们将开发新的计算方法来模拟微生物代谢，并使用结果来优化实验室中的生长。当与实验人员密切合作开发时，模型可以有效地指导实验设计，并且模型也是安全和廉价的。计算模型是分析复杂交互网络的越来越重要的工具，因为它们可以在大型复杂系统中包含许多交互。我们将开发代表代谢网络的模型和算法来分析这些模型，并指导实验进行最佳修改以改善生长。基础模型将使用基因组序列信息和基因表达数据来确定细菌中存在的酶，从而确定哪些反应在不同条件下进行。在此基础上，结合我们在实验室收集的数据，我们将预测和测试优化代谢网络的方法，例如找到允许在最便宜的培养基上生长最多的营养条件。我们将使用细菌百日咳杆菌作为模型系统。相关的基因组序列和基因表达数据是可用的，并且B.百日咳有一个有趣的代谢，并将提供一个不同的角度从以前的方法开发工作，主要集中在大肠杆菌，虽然方法本身已被应用于其他生物。我们将开发优化算法来预测改善B增长的方法。通过改变生长培养基或通过基因改变来促进生长。我们将在实验室实验中测试这些预测，以验证和完善我们开发的新方法，并开发其应用。理论建模与实验测试相结合是一种强有力的方法，上级纯理论系统。应用和益处该提案将开发新的方法，在微生物代谢的计算模型中使用基因组序列信息和基因表达数据。全基因组测序的成本正在迅速下降，而基因组测序中心生成数据的能力正在迅速增加。因此，迫切需要新的方法来解释和利用基因组数据。从单基因研究向基因组水平研究的转变促进了对生物体比以前更全面的看法，并激发了基因组规模，基于系统的研究方法。因此，本提案中提出的概念和方法将广泛适用于使用基因组序列数据的其他研究。生成的数据也将被广泛使用。代谢是所有细菌生理学的基础，因此我们的研究所获得的更广泛的代谢视角引起了广泛的关注。改进了B的生长方法。百日咳对大规模培养这种细菌的生物技术工业部门，如疫苗制造商，将是有价值的。因此，尽管我们使用B。百日咳作为一种模式生物，用于开发新的系统生物学方法，这将产生直接影响学术界以外的产出。

Context Microbial metabolism consists of networks of reactions that bacteria use to convert nutrients into molecules that make up the cell and release energy for cell processes. Such networks have an enormous potential to produce a huge range of molecules that might be useful, for example as biofuels or biopharmaceuticals, but these are rarely produced at high levels. Genome sequencing has revealed metabolic pathways that produce these molecules, and genetic modifications of these pathways allow us to manipulate microbes for biotechnology purposes. However, the high complexity of bacterial metabolism has considerably limited such efforts. This calls for new approaches that can incorporate the complexity of metabolic systems and predict appropriate modifications. Aims We will develop novel computational approaches to modelling microbial metabolism, and use the results to optimise growth in the laboratory. When developed in close collaboration with experimentalists, models can effectively guide experiment design, and models are also safe and inexpensive to work with. Computational models are increasingly important tools to analyse complex interaction networks, as they can incorporate many interactions in large complex systems. We will develop models representing metabolic network,s and algorithms to analyse these and direct experiments towards optimal modifications to improve growth. The underlying model will use genome sequence information and gene expression data to determine the enzymes that are present in the bacterium and thus which reactions are operating under different conditions. Based on this, together with data we collect in the laboratory, we will predict and test ways to optimise the metabolic network, for example to find nutrient conditions that permit the most growth on the least expensive medium. We will use the bacterium Bordetella pertussis as a model system. The relevant genome sequence and gene expression data are available, and B. pertussis has an intriguing metabolism and will provide a different perspective from previous method development work, which has focussed largely on E coli, although the methods themselves have been applied in other organisms. We will develop optimisation algorithms to predict ways to improve the growth of B. pertussis either through altered growth media or by genetic alterations to enhance growth. We will test these predictions in laboratory experiments to validate and refine the novel methods we develop, and to develop its applications. The combination of theoretical modelling with experimental testing is a powerful approach that is superior to purely theoretical systems. Applications and benefits This proposal will develop new methods to use genome sequence information and gene expression data in computational models of microbial metabolism. The cost of whole genome sequencing is dropping rapidly while the capacity of genome sequencing centers to generate data is rapidly increasing. Thus new approaches to interpret and exploit genomic data are needed urgently. The move away from single-gene studies towards genome level studies facilitates a more holistic view of an organism than before and motivates genome-scale, systems-based research approaches. The concepts and approaches developed in this proposal will thus be widely applicable to other studies using genome sequence data. The data generated will also be widely usable. Metabolism is fundamental to the physiology of all bacteria, so the wider perspective of metabolism gained by our studies is of interest to a broad audience. Improved growth methods for B. pertussis will be valuable to sectors of the biotechnology industry that grow this bacterium on a large scale, such as vaccine manufacturers. Thus, although we are using B. pertussis as a model organism for the development of novel systems biology methods, this will generate outputs that have immediate impact outside of academia.