Metabolic Drug Targeting in Haemophilus influenze

描述（申请人提供）：随着细菌不断进化和遇到新的传染性生物，抗菌药物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将来的这些感染。代谢是未来抗菌药物开发的潜在丰富靶点来源，尚未开发。识别代谢靶点并从机理角度以高分辨率理解其基本作用的能力对于治疗方法的开发是理想的。利用代谢网络组件的固有相似性和差异以及许多生物体的代谢功能，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，我们提出了一个SBIR计划，该计划采取了最初的第一步，旨在建立一个模型驱动的技术平台，该平台能够生成利用这类潜在的丰富目标所必需的代谢洞察力和表征。我们将使用基于约束的建模方法和SimPheny的强大功能来进行建模和实验数据的综合分析。通过改进现有的H. SimPheny中的流感病毒和本文提出的工作，该计划将产生：1）改进的H.流感病毒代谢途径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，以提供高通量数据集（即基因/蛋白质表达谱）的综合分析的背景; 3）可扩展的平台，以展示使用模型设计实验计划和指导生物发现的能力; 4）用于开发H.流感感染; 5）和增强验证现有的目标，在H。流感。

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.