Systems Biology Approach to Growth Regulation in Cystic Fibrosis

描述（由申请人提供）：患有囊性纤维化（CF）的个体通常会死于其疾病的肺部并发症，但 CF 涉及所有主要器官系统。 CF 患者的存活率与体重指数密切相关，小鼠模型表明 CF 患者的体型大小是由于神经内分泌功能、脂肪代谢和肠道功能的复杂相互作用所致。我们将继续利用小鼠模型来阐明所涉及的途径及其相互作用，在这里我们提出了一个计划，该计划将调用生物信息学从文献和数据库中提取途径信息，并筛选出与我们的经验数据一致的信息。根据这些途径，我们将开发计算模型，以预测这些途径在以特定方式受到干扰时的结果。使用遗传和药理学方法，我们将扰乱这些途径并评估它们预测结果的效果。来自这些操作的数据将被输入到生物信息学处理中，并且将生成和测试新的或修订的模型，直到生成真正的预测模型。与此同时，一项全基因组关联研究正在进行，并将检查显示与肺部疾病或体重指数相关的基因，以确定它们是否可以放置在小鼠研究生成的通路图上。通过这个迭代过程，我们希望确定有助于 CF 病理生理学的新途径和/或阐明已知影响 CF 严重程度的途径的作用。

DESCRIPTION (provided by applicant): Individuals afflicted with cystic fibrosis (CF) typically succumb to pulmonary complications of their disease, but CF involves all of the major organ systems. Survival of CF patients correlates strongly with body mass index, and mouse models indicate that body size in CF is due to a complex interaction of neuroendocrine function, adipose metabolism and intestinal function. We will continue to utilize mouse models to elucidate the pathways involved and their interactions, and here we propose a plan that will invoke bioinformatics to extract pathway information from literature and databases and sort out those that are concordant with our empiric data. From those pathways, we will develop computational models that will predict outcomes of these pathways when perturbed in specific ways. Using genetic and pharmacologic methods, we will perturb those pathways and assess how well they predict outcomes. Data from these manipulations will be entered back into the bioinformatics processing and new or revised models will be generated and tested until truly predictive models are generated. Concurrently, a whole-genome association study is being carried out and genes showing association with pulmonary disease or body mass index will be examined to determine if they can be placed on the pathway maps generated by the mouse studies. By this iterative process, we hope to identify new pathways contributing to CF pathophysiology and/or clarify the role of pathways known to influence CF severity.