New Integrative Pathway Analysis Methods to Predict Biomedical Outcomes

这项研究的长期目标是揭示决定通常秩序的关键监管机构。 动物从未分化的多能细胞发育为执行所有功能的特化细胞。 在我们的身体中发挥作用。基因组的协调表达是这些过程的基础， 由相互作用的基因组成的网络所精心策划，而我们才刚刚开始揭开这个网络的面纱。细胞回路很复杂， Yamanaka因子的发现表明，即使是很少的转录因子， 对细胞和组织的命运产生深远的影响。因此，解锁细胞所需的基因组合 决定因素似乎过于吝啬。大规模的项目正在进行中， 表观基因组和功能基因组景观的许多不同的细胞在多个不同的生物体。一样高- 随着诸如DNA和RNA测序等通量技术的成熟，对DNA和RNA测序的需求增加。 整合的方法来阐明内在的，适应性的和编程的表型的规则 细胞经历的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发展起来的途径综合框架 用于解释癌症基因组图谱项目的癌症基因组数据集。的扩展 将开发所使用的输入途径，并在模型中进行改进，以丰富形式表示， 可以分析人类和模式生物体的广泛数据集。该方法将最终在 将机器学习分类与概率图模型相结合。分类器将识别 在一个大的数据库中的细胞状态区别的预测途径特征。这些基因操作 然后，可以以任何组合提出特征，作为对 产生的分类器，这项工作的一个主要优势。该途径模型将用于预测 可以赋予人类皮层神经元分化和去分化队列的因素。计算上 将在活细胞中测试该系统中预测的基因扰动。识别细胞的关键调节剂 作为干细胞向神经祖细胞转化为成熟神经细胞类型的基础的命运决定 推进我们对神经发育的理解这些监管机构也可能在以下方面发挥重要作用： 神经胶质瘤，一种肿瘤细胞似乎处于神经祖细胞样状态的疾病。 总之，所提出的理论和应用信息学方法将有助于强有力的工具， 解释和预测常规和异常细胞反应。研究人员将能够查询 用计算机算法作为高保真度代理的复杂网络。在不远的将来，我们希望 以促进我们对正常分化的理解，并阐明这些程序的调节 在癌症等疾病过程中分解，为诊断，预后和治疗提供了线索 战略布局

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.