Metabolic Drug Targeting in Haemophilus influenze

描述(申请人提供)：随着细菌的不断进化和遇到新的感染性生物，抗菌素耐药性的上升持续存在。这种情况迫切需要开发新的化合物来治疗现在和未来的这些感染。新陈代谢是未来抗菌药物开发的潜在丰富靶点来源，但尚未开发。从机械学的角度识别代谢目标并在高水平的分辨率下理解它们的基本作用的能力是治疗学发展的理想选择。利用许多生物体代谢网络组成和功能能力的内在相似性和差异性，可以提供前所未有的能力来设计窄谱和广谱抗生素，提供必要的计算/实验基础设施。 在此，我们提出了SBIR计划，该计划朝着建立一个模型驱动的技术平台迈出了初步的第一步，该平台能够产生开发这一潜在丰富目标类别所需的代谢洞察和特征。我们将使用已建立的基于约束的建模方法和SimPheny的强大功能来执行实验数据的建模和综合分析。通过增强SimPheny内现有的流感嗜血杆菌模型和本计划建议的工作，该计划将产生：1)改进流感嗜血杆菌代谢途径的特征及其在侵袭性疾病中的调节和作用；2)代谢系统的必要表示，为高通量数据集(即基因/蛋白质表达谱)的综合分析提供背景；3)一个可扩展的平台，以展示使用模型来设计实验计划和指导生物发现的能力；4)针对流感嗜血杆菌感染的抗菌剂的新靶点的开发；5)加强对流感嗜血杆菌现有靶点的验证。

DESCRIPTION (provided by applicant): The rise of antimicrobial drug resistance persists as bacteria continue to evolve and new infectious organisms are encountered. This situation has created an imperative need to develop new compounds to treat these infections both now and in the future. Metabolism represents a potentially rich source of targets for future antimicrobial drug development that remains untapped. The ability to identify metabolic targets and understand their essential roles at a high level of resolution from a mechanistic perspective is ideal for the development of therapeutics. Taking advantage of the inherent similarities and differences in the metabolic network components and functional capabilities of metabolism across many organisms may offer an unprecedented ability to design narrow as well as broad spectrum antibiotics, provided the necessary computational/experimental infrastructure. Herein we put forth an SBIR program that takes the initial first steps toward building a model-driven technology platform capable of generating the metabolic insight and characterization necessary to exploit this potentially rich class of targets. We will be using established approaches from constraints-based modeling and the powerful capabilities of SimPheny to perform modeling and integrative analysis of experimental data. Through the enhancement of an existing model of H. influenzae within SimPheny and the work proposed herein this program will generate: 1) an improved characterization of H. influenzae metabolic pathways and their regulation and role in invasive disease; 2) the necessary representation of metabolic systems to provide the context for integrative analysis of high throughput data sets (i.e. gene/protein expression profiles); 3) a scalable platform to demonstrate the capability of using models to design experimental plans and guide biological discovery; 4) novel targets for the development of antimicrobials for H. influenzae infections; 5) and enhanced validation of existing targets in H. influenzae.