Integrative Modeling of Regulatory Processes Using High-Throughput Genetic Data

描述（由申请人提供）：我们提出了一个跨学科的努力，连接计算和实验方法来分析和模拟两个过程中不可或缺的C。优雅生理学：程序性细胞死亡和种系干细胞增殖决策边界的调节。广泛的目标是量化表型变异，使用图像分析和模式识别工具，使用提取的功能和基因表达数据来开发一个因果模型的监管过程，并验证模型的实验。第一个目标是集中在数据收集和高通量图像采集和处理形式的表型的自动评分。表型数据的范围包括来自两个亲本C. elegans菌株。正在调查的复杂的监管过程涉及细胞水平的属性，通常通过显微镜测定。我们提出模式识别算法来自动化表型评分。第二个目标提出了新的方法来建立综合模型的联合基因型，表达和表型数据集。我们讨论了组合的基因组数据如何为学习因果模型提供巨大的潜力。我们提出了一个学习框架的基础上，一个新的模块化贝叶斯网络平台，有效地降低噪声和数据的复杂性。我们详细介绍了使用复杂的优化技术，旨在避免局部最优的模型评分过程。第三个目标涉及使用我们的模型来指导实验，以努力剖析表型背后的遗传基础。因果模型提供了一个解释，遗传变异如何通过调节基因表达导致表型变化。因此，因果模型可以被认为是“生物学假设”，并且可以从模型中推断出有希望的实验候选者。拟议的研究将开发新的技术，用于综合来自多个数据源的信息，并将这些方法与模型研究动物C.优雅计算方法和实验分析的协同相互作用将为大大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方面具有变革性，而且在构成工作基础的创新计算研究方面也具有变革性。 公共卫生相关性：我们将使用最近的高通量生物数据，包括基因表达，亲本基因型和生理性状的测量，开发复杂的生物调控过程的分析计算模型。我们的模型旨在解释各种生理性状如何根据基因表达状态和亲本基因型而变化。蠕虫C.秀丽线虫提供了丰富的有趣的生理特征来源，这些特征在遗传学方面是高度复杂的。因此，这些模型将有助于理解复杂的疾病，如癌症。

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. PUBLIC HEALTH RELEVANCE: We will develop computational models for the analysis of complex biological regulatory processes using recent high-throughput biological data, including measurements of gene expression, parental genotype and physiological traits. Our models are designed to explain how various physiological traits change depending on the state of gene expression and parental genotype. The worm C. elegans offers a rich source of interesting physiological traits that are highly complex in terms of genetics. Therefore, these models will be useful for understanding complex diseases such as cancer.