Integrative Modeling of Regulatory Processes Using High-Throughput Genetic Data

描述(由申请人提供)：我们提出了一种结合计算和实验方法的跨学科努力，以分析和模拟线虫生理中不可或缺的两个过程：程序性细胞死亡和生殖系干细胞增殖决策边界的调节。其主要目标是使用图像分析和模式识别工具来量化表型变异，使用所提取的特征和基因表达数据来为调控过程开发因果模型，并在实验上验证该模型。第一个目标是以高通量图像采集和处理的形式集中于数据收集和表型的自动评分。表型数据的范围包括来自两个亲本线虫菌株的重组后代。正在研究的复杂调控过程涉及细胞水平的属性，这些属性通常通过显微镜进行分析。我们提出了模式识别算法来自动进行表型评分。第二个目的是提出一种新的方法来建立联合基因、表达和表型数据集的综合模型。我们讨论了组合基因组数据如何为学习因果模型提供了巨大的潜力。提出了一种基于新型模块化贝叶斯网络平台的学习框架，有效地降低了噪声和数据复杂度。我们详细阐述了在模型评分过程中如何使用复杂的优化技术来避免局部最优。第三个目标涉及使用我们的模型来指导实验，努力剖析表型背后的遗传基础。因果模型解释了遗传变异如何通过调节基因表达而导致表型变化。因此，因果模型可以被认为是“生物学假说”，并且可以从这些模型中推断出有希望的实验候选者。这项拟议的研究将开发从多个数据源合成信息的新技术，并将这些方法与模式研究动物线虫的遗传变异的实验研究相结合。计算方法和实验分析的协同作用将为极大地加速生物发现提供一个范例。拟议的研究不仅在为领域科学家提供的功能上具有变革性，而且在形成工作基础的创新计算研究方面也具有变革性。

DESCRIPTION (provided by applicant): We propose an interdisciplinary effort linking computational and experimental methods to analyze and model two processes integral to C. elegans physiology: programmed cell death and the regulation of the germline stem cell proliferation decision boundary. The broad aims are to quantify phenotypic variation using image analysis and pattern recognition tools, to use the extracted features and gene expression data to develop a causal model for the regulatory processes, and to validate the model experimentally. The first aim is focused on data collection and the automated scoring of phenotypes in the form of high throughput image acquisition and processing. The scope of the phenotypic data encompasses recombinant progeny from two parental C. elegans strains. The complex regulatory processes under investigation involve cellular-level attributes that are typically assayed via microscopy. We propose pattern recognition algorithms to automate phenotypic scoring. The second aim proposes novel methodology to build integrative models over the joint genotypic, expression and phenotypic datasets. We discuss how the combined genomic data offer immense potential for learning causal models. We propose a learning framework based on a novel modular Bayesian network platform that effectively reduces noise and data complexity. We elaborate on the use of complex optimization techniques designed to avoid local optima in the model scoring procedure. The third aim involves using our models to direct experimentation in efforts to dissect the genetic bases underlying the phenotypes. The causal models provide an explanation of how genetic variations lead to phenotypic change by modulating gene expression. Thus, the causal models can be thought of as "biological hypotheses," and promising experimental candidates can be inferred from the models. The proposed research will develop new techniques for synthesizing information from multiple data sources and will integrate these methods with experimental studies of genetic variation in a model research animal, C. elegans. The synergistic interplay of computational methods and experimental analyses will provide a paradigm for greatly accelerating biological discovery. The proposed research is transformative not only in the functionality that it offers to domain scientists but also in the innovative computational research that forms the basis for the work.