New Integrative Pathway Analysis Methods to Predict Biomedical Outcomes

描述（由申请人提供）：这项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体内所有功能的特化细胞的通常有序发育的关键调控因子。基因组的协调表达是这些过程的基础，并由我们刚刚开始揭开的相互作用的基因网络精心策划。细胞回路是复杂的，但Yamanaka因子的发现表明，即使不到少数转录因子也可以对细胞和组织的命运产生深刻的变化。因此，解开细胞决定因子所需的基因组合似乎过于简单。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。随着诸如DNA和RNA测序的高通量技术的成熟，对阐明细胞经历的内在的、适应性的和程序化的表型变化的潜在规则的整合方法的需求增加，所述内在的、适应性的和程序化的表型变化可以从这样的数据推断。我们的出发点将是扩展过去几年开发的途径整合框架，用于解释癌症基因组图谱项目的癌症基因组数据集。扩展所使用的输入途径，并在模型中的进步，以丰富的正式表示，将开发，以便在人类和模式生物的数据集的广度可以进行分析。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别的预测途径特征。这些功能之间的遗传操作，然后可以提出，在任何组合，作为正式的干预所产生的分类器的图形模型，这项工作的一个主要优势。通路模型将被应用于预测的因素，可以赋予分化和去分化队列人类皮层神经元。将在活细胞中测试该系统中计算预测的基因扰动。确定干细胞转化的细胞命运决定的关键调节剂 细胞到神经祖细胞再到成熟神经细胞类型将促进我们对神经发育的理解。这些相同的调节因子也可能在神经胶质瘤中发挥重要作用，神经胶质瘤是一种肿瘤细胞似乎处于神经祖细胞样状态的疾病。两者合计，提出的理论和应用信息学方法将有助于解释和预测常规和异常细胞反应的强大工具。研究人员将能够用计算机算法作为高保真度的替代品来查询复杂的网络。在不久的将来，我们希望能够推进我们对正常分化的理解，并阐明这些程序的调节如何在癌症等疾病过程中被破坏，从而阐明诊断，预后和治疗策略。

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.