Modeling Core

项目总结(见说明)： 模型核心将整合项目1和2的数据，并通过基于回归的对生物间对基因表达的直接和间接影响的推断，构建一个联合寄主和病原体基因调控网络(GRN)模型。这一跨物种GRN模型将把数量表型与影响结核病感染结果的因果环境和遗传因素联系起来，特别是敏感性、耐药性、感染进展、持久性和清除。 这些预测模型的核心是机器学习算法-cMonkey和Inferelator-它们将协同工作，发现受组合环境(如pH)和遗传(如转录因子)影响有条件地共同调节的一组基因。结合到这些算法中的数据集成策略将克服几个挑战：(1)系统生物学数据中的技术和生物噪声；(2)缺乏基因组中超过50%的基因的功能信息；(3)缺乏调控机制的详细知识；以及(4)对宿主和MTB网络都已适应的环境空间的不完整知识。建模核心将直接从数据对GRN的架构进行逆向工程，同时学习转录调控的相关动态。此外，未知功能的基因将根据它们的共表达模式以及其他共同特征(如相互作用、系统发育和顺式调控元件)整合到网络中，从而有可能发现可能对感染结果至关重要的其他基因。 MTB和BMMO的GRN模型已经初步建立，对这些模型的分析表明，它们概括了现有的知识，并预测了可能影响MTB感染结局的新基因。这些模型预测为两个项目的实验设计和优先事项提供了指导。

PROJECT SUMMARY (See instructions): The Modeling Core will integrate data from Projects 1 and 2 and construct a joint host and pathogen gene regulatory network (GRN) model by regression-based inference of direct and indirect inter-organismal influences on gene expression. This cross-species GRN model will link quantitative phenotypes to causal environmental and genetic triggers that influence the outcome of TB infection, specifically susceptibility, resistance, infection progression, persistence, and clearance. Central to these predictive models are machine learning algorithms - cMonkey and Inferelator - that will work in tandem to discover groups of genes that are conditionally co-regulated by combinatorial environmental (e.g. pH) and genetic (e.g; transcription factors) influences. The data integration strategies incorporated into these algorithms will overcome several challenges: (1) technical and biological noise in systems biology data; (2) lack of functional information for over 50% of all genes in the genome; (3) lack of detailed knowledge of regulatory mechanisms; and (4) incomplete knowledge of the environmental space to which both the host and MTB networks have adapted. The Modeling Core will reverse engineer the architecture of GRNs directly from data while simultaneously learning the associated dynamics of transcriptional regulation. Moreover, genes of unknown function will be integrated into the network based on their co-expression patterns, and other shared features such as interactions, phylogeny, and cis-regulatory elements; making it possible to discover additional genes that might be critical for outcome of infection. Preliminary GRN models for both MTB and BMMO have already been constructed, and analysis of these models has demonstrated that they recapitulate existing knowledge and predict new genes that might, influence the outcome of MTB infection. These model predictions have provided guidance for experimental designs and priorities in both Projects.