New Integrative Pathway Analysis Methods to Predict Biomedical Outcomes

描述（由申请人提供）：本研究的长期目标是揭示决定动物从未分化的多能细胞到执行我们身体所有功能的特化细胞通常有序发育的关键调节因子。基因组的协调表达是这些过程的基础，是由相互作用的基因网络精心策划的，我们才刚刚开始揭开谜底。细胞回路是复杂的，但山中因子的发现表明，即使是少数转录因子也能对细胞和组织的命运产生深远的变化。因此，解锁细胞决定因子所需的基因组合似乎非常少。大规模的项目正在进行中，对多种不同生物体中许多不同细胞的基因组、表观基因组和功能基因组景观进行编目。随着DNA和RNA测序等高通量技术的成熟，对综合方法的需求增加，以阐明细胞经历的内在、适应性和程序性表型变化的规则，这些变化可以从这些数据中推断出来。我们的出发点将是扩展过去几年开发的通路整合框架，用于解释癌症基因组图谱项目的癌症基因组数据集。对所使用的输入路径的扩展，以及在模型中丰富形式表示的进展，将被开发，以便可以分析人类和模式生物的广泛数据集。该方法最终将机器学习分类与概率图模型相结合。分类器将在大型数据库中识别细胞状态区分的预测路径特征。这些特征之间的遗传操作可以在任何组合中提出，作为对结果分类器的图形模型的正式干预，这是这项工作的主要优势。该通路模型将被应用于预测能够赋予人类皮层神经元分化和去分化队列的因素。在这个系统中计算预测的基因扰动将在活细胞中进行测试。识别干细胞转化下细胞命运决定的关键调节因子

DESCRIPTION (provided by applicant): The long-term goal of this research is to reveal the key regulators that determine the usually ordered development of an animal from undifferentiated pluripotent cells to specialized cells that carry out all of the functions in our body. The coordinated expression of the genome underlies these processes and is orchestrated by networks of interacting genes that we are only beginning to unveil. Cell circuitry is complex but the discovery of the Yamanaka factors demonstrates that even less than a handful of transcription factors can exert profound changes on cell and tissue fates. Thus, the combinations of genes needed to unlock cell determinants seem tantalizingly parsimonious. Large-scale projects are underway to catalog the genomic, epigenomic, and functional genomic landscapes of many different cells in multiple different organisms. As high- throughput techniques such as DNA and RNA sequencing mature, there is an increase in demand for integrative approaches to elucidate the rules underlying intrinsic, adaptive, and programmed phenotypic changes that cells undergo that can be inferred from such data. Our starting point will be to extend the pathway integrative framework developed over the past several years for the interpretation of cancer genomics datasets for the Cancer Genome Atlas project. Extensions to the input pathways used, and advances in the model to enrich the formal representation, will be developed so that a breadth of datasets in human and model organisms can be analyzed. The approach will culminate in the combining of machine-learning classification with probabilistic graphical models. The classifiers will identify predictive pathway features for cell state distinctions in a large database. Genetic manipulations among these features can then be proposed, in any combination, as formal interventions on the graphical model of the resulting classifiers, a major advantage of this work. The pathway models will be applied to the prediction of factors that can confer differentiation and de-differentiation queues to human cortical neurons. Computationally predicted gene perturbations in this system will be tested in living cells. Identifying critical modulators of the cell fate decisions underlying the conversion of stem cells to neural progenitors to mature neural cell types will advance our understanding of neural development. These same regulators may also play an important role in glioma, a disease where the tumor cells appear to be in a neural progenitor-like state. Taken together, the proposed theoretical and applied informatics approaches will contribute powerful tools for interpreting and predicting both routine and aberrant cellular responses. Researchers will be able to query the complex networks with computer algorithms as high fidelity surrogates. In the not so distant future, our hope is to advance our understanding of normal differentiation and shed light on how the regulation of these programs breaks down in disease processes like cancer, shedding light on diagnostic, prognostic, and therapeutic strategies.