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有序的 动物从未分化的多能细胞发育成专门的细胞，执行所有 在我们的身体里发挥作用。基因组的协调表达是这些过程的基础，也是 由我们刚刚开始揭开的相互作用的基因网络编排。电池电路很复杂，但 山中因子的发现表明，即使不到几个转录因子也能 对细胞和组织的命运产生深刻的影响。因此，解锁细胞所需的基因组合 决定因素看起来非常吝啬。正在进行大规模的项目来对基因组进行分类， 多个不同生物中许多不同细胞的表观基因组和功能基因组图景。有那么高- 随着DNA和RNA测序等吞吐量技术的成熟，对 用综合方法阐明内在、适应性和程序性表型的潜在规则 细胞所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的路径综合框架 用于为癌症基因组图谱项目解释癌症基因组数据集。扩展到 将开发所使用的输入路径以及模型中丰富形式表示的进展，以便 可以分析人类和模型生物体中的大量数据集。这一方法将在 将机器学习分类与概率图形模型相结合。分类器将识别 大型数据库中细胞状态差异的预测路径特征。其中的基因操作 然后，可以以任意组合的形式提出功能，作为对图形模型的正式干预 由此产生的量词，这是这项工作的一大优势。路径模型将被应用于预测 可以赋予人类皮质神经元分化和去分化队列的因子。从计算上讲 该系统中预测的基因扰动将在活细胞中进行测试。确定细胞的关键调节器 从干细胞到神经前体细胞再到成熟神经细胞类型的命运决定将 加深了我们对神经发育的理解。这些监管机构也可能在 神经胶质瘤是一种肿瘤细胞似乎处于神经前体细胞样状态的疾病。 综上所述，提出的理论和应用信息学方法将为 解释和预测常规和异常细胞反应。研究人员将能够质疑 使用计算机算法作为高保真代理的复杂网络。在不远的将来，我们的希望是 以增进我们对正常分化的理解，并阐明如何监管这些项目 分解癌症等疾病过程，为诊断、预后和治疗提供帮助 战略。

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.