Mathematical modeling from metagenomics - minimizing risk of enteric infections

 描述：肠道感染是当今医疗保健中的一个关键问题。最近对DNA测序数据的分析表明，这种感染与广谱抗生素的预防性治疗有关。这是由于它们在杀死本地肠道微生物群方面的作用，而本地肠道微生物群通常对抗病原体。对这些数据的计算分析应该有助于优化抗生素和粪便移植策略。这还不可能，因为目前使用的方法是基于相关性的。该项目的主要目标是将最近开发的和新的数学建模工具与代谢途径、推理和实验相结合，以预测肠道疾病的风险，并构建合理设计的粪便移植疗法的原型，将其降至最低。在前期工作的基础上，PI和合作者建议：使用16S rRNA约束的数学模型预测所有稳定的、调节临床相关肠道病原体定植的微生物群；将风险回归建模与微生物群动力学预测相结合，以评估住院患者肠道感染的风险；确定 与本地肠道细菌和肠道病原体之间的相互作用相关的微生物代谢途径；通过对建模预测的实验验证，基于原型建模的粪便移植策略。设计合理的治疗方法，将肠道疾病的发病率降至最低，取决于我们对肠道微生物区系调控的动态的了解。因此，拟议的研究是及时的，并与国家国际开发署的使命相关。将新的预测模型应用于来自大量住院患者的DNA测序数据，将使我们能够识别具有益生菌(和非生态菌)特性的微生物区系状态，从而成为治疗的靶点。微生物动态的预测，与基于风险分析的新统计模型相结合，将提供第一个计算 用于准实时监测肠道疾病风险的工具。代谢重建方法在数学模型预测中的应用将为潜在的代谢机制提供新的见解，这些机制调节和负责病原菌难治和相容稳定稳定状态的预测稳定性。对模型预测的实验验证，不仅将允许评估所开发的数学框架的预测能力，还将提供测试所提出的合理设计的粪便移植策略的有效性的机会。

 DESCRIPTION: Enteric infections represent a critical issue in today's healthcare. Recent analysis of DNA sequencing data has demonstrated that such infections are associated with the prophylactic treatment with broad-spectrum antibiotics. This is due to their role in killing the native intestinal microbiota, which normally antagonizes pathogens. Computational analysis of these data should facilitate the optimization of antibiotic and fecal transplantation strategies. This is not yet possible because the currently used methods are based on correlations. The main goal of this project is to combine recently developed and novel mathematical modeling tools with metabolic pathways inference and experimentation to predict the risk of enteric diseases and to prototype rationally designed fecal transplantation therapies to minimize it. Leveraging on preliminary work, the PI and collaborators propose to: predict all the stable microbiota profiles mediating colonization by clinically-relevant enteric pathogens using 16S rRNA-constrained mathematical models; combine hazard regression modeling with microbiota dynamics predictions to evaluate the risk of enteric infections in hospitalized patients; determine microbial metabolic pathways associated with the interactions between native intestinal bacteria and enteric pathogens; prototype modeling-based fecal transplantation strategies by experimental validation of modeling predictions. The design of rational therapies minimizing the incidence of enteric diseases depends on our understanding of the dynamics regulating the intestinal microbiota. For this reason, the proposed research is timely and relevant to the mission of the NIAID. The application of new predictive models to DNA sequencing data from a large population of hospitalized patients will allow identifying microbiota states with probiotic (and dysbiotic) properties to be targeted by therapies. The forecasting of microbial dynamics, combined with novel statistical models based on risk analysis, will deliver the first computational tool for monitoring the risk of enteric diseases in quasi-real time. The application of metabolic reconstruction methods to the mathematical modeling predictions will provide new insights about potential metabolic mechanisms regulating and responsible for the predicted stability of pathogen-refractory and compatible stable steady states. The experimental validation of the modeling predictions, not only will allow evaluating the predictive power of the developed mathematical frameworks, but also will provide the opportunity to test the efficacy of the proposed rationally designed fecal transplantation strategies.