Modeling Core

建模核心

• 批准号: 8577280
• 负责人: Nitin S Baliga
• 金额: $ 77.46万
• 依托单位: SEATTLE BIOMEDICAL RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-21 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8577280
• 关键词: Algorithms; Architecture; Bacteria; Biological; Biology; Cell model; Cells; Cessation of life; Clinical; Computational Biology; Data; Disease; Disease Progression; Ensure; Experimental Designs; Gene Expression; Genes; Genetic; Genome; Goals; Human; Immune response; Infection; Instruction; Joints; Knock-out; Knowledge; Leadership; Learning; Link; Machine Learning; Measurement; Measures; Microbiology; Modeling; Modification; Mycobacterium tuberculosis; Network-based; Noise; Outcome; Pathway interactions; Pattern; Phase; Phenotype; Phylogeny; Predisposition; Regulator Genes; Regulatory Element; Resistance to infection; Role; Small Interfering RNA; System; Systems Biology; Training; Transcriptional Regulation; Tuberculosis; Work; base; biological systems; combinatorial; data integration; follow-up; insight; macrophage; multi-scale modeling; network models; novel; pathogen; predictive modeling; research study; response; trait; transcription factor

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.

项目摘要（请参阅说明）： 建模核心将通过基于回归的直接和间接有机体间影响基因表达的推断来整合项目1和2的数据，并通过基于回归和间接的基因间影响来构建关节宿主和病原体基因调节网络（GRN）模型。这种跨物种的GRN模型将定量表型与影响结核病感染结果的因果环境和遗传触发因素，特别是易感性，耐药性，感染进展，持久性和清除率。 这些预测模型的核心是机器学习算法 - cmonkey和地狱 - 将同时起作用，以发现由组合环境（例如pH）和遗传（例如，转录因子）影响的基因组。这些算法中纳入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）缺乏基因组中所有基因超过50％的功能信息； （3）缺乏监管机制的详细知识； （4）对主机和MTB网络已经适应的环境空间的不完整知识。建模核心将直接从数据中反向工程GRN的体系结构，同时学习转录调节的相关动力。此外，未知功能的基因将根据其共表达模式集成到网络中，以及其他共享特征，例如相互作用，系统发育和顺式调节元素。可以发现可能对感染结果至关重要的其他基因。 MTB和BMMO的初步GRN模型已经构建，对这些模型的分析表明，它们概括了现有知识并预测可能影响MTB感染结果的新基因。这些模型预测为两个项目的实验设计和优先级提供了指导。