Theoretical Foundations and Software Infrastructure for Biological Network Databases

 描述(申请人提供)：生物分子之间不断增长的物理、功能和统计相互作用数据，从DNA调节区、功能RNA、蛋白质、代谢物、脂类，以及基因组变体中的数据，为计算发现和构建细胞机制的统一系统视图提供了前所未有的机会。这些数据和相关的形式主义使系统方法成为可能，从而导致了生物医学科学的独特进步。然而，不幸的是，网络数据的存储方案、数据结构、表示和查询机制要比其他的“at”或低维数据表示(例如，序列或分子表达式)复杂得多。当我们考虑细胞中可能发生的相互作用的异质性时，这种复杂性更加明显。例如，一对编码蛋白质的基因可以通过多种方式相互作用：i)我们可以模拟它们基因产物之间的物理相互作用，或者它们的蛋白质-蛋白质相互作用，ii)一个基因产物与另一个基因的启动子/增强子/沉默区域的相互作用，或iii)双突变之间的遗传相互作用，其表型明显不同于单个突变的组合。当人们考虑到不同版本的数据集、用于分析和收集分子之间相互作用的不同技术、跨数据的链接以及与其他工具的接口时，这种复杂性更加明显。该项目寻求回答与高效利用大型网络结构数据集相关的一些基本问题： 大型网络结构数据库的最佳存储方案有哪些(可以证明)？应该如何存储相同/相关数据集的多个版本？如何在压缩和查询效率之间进行权衡？以及如何适当地提取网络数据，以便用户可以使用Cytoscape等前端交互地询问它们？该项目旨在通过开发具有理论基础和计算验证的存储方案、算法和软件来回答这些问题，这些存储方案、算法和软件将使生物网络能够高效和有效地存储、更新、处理和查询。我们将开发用于高效存储的压缩技术和版本控制机制，允许用户创建他们自己的网络版本，开发用于在这些网络上进行高效查询处理的算法，并将这些算法实施到广泛可访问和用户友好的软件中。这项研究将产生新的计算工具，这些工具将以开放源码公共领域软件的形式传播给社区。我们的工具将使生物医学科学中更广泛的社区从根本上更容易获得网络数据。这将使网络数据在应用中更常见，包括识别复合预后和诊断标记、疾病基因优先排序、肿瘤异质性和癌症进展的建模、通知治疗、确定治疗靶点和药物重新定位。从这些角度来看，算法和软件都有着深远而深刻的影响。

 DESCRIPTION (provided by applicant): Ever-increasing amounts of physical, functional, and statistical interaction data among bio-molecules, ranging from DNA regulatory regions, functional RNAs, proteins, metabolites, lipids, as well as those among genomic variants, offer unprecedented opportunities for computational discovery and for constructing a unified systems view of the cellular machinery. These data and associated formalisms have enabled systems approaches that led to unique advances in biomedical sciences. Unfortunately, however, storage schemes, data structures, representations, and query mechanisms for network data are considerably more complex, compared to other, "at" or low-dimensional data representations (e.g., sequences or molecular expression). This complexity is even more evident when we consider heterogeneity of possible interactions that can occur in the cell. For example, a pair of protein-coding genes can interact in a variety of ways: i) we can model physical interactions between their gene products, or their protein-protein interactions, ii) inter- action of a gene product with the promoter/enhancer/silencer region of the other gene, or iii) genetic interaction among double-mutants with significantly different phenotype than the effect of single mutations combined. This complexity is further evident, when one considers different versions of datasets, different techniques used for assaying and gathering the interactions between molecules, linkages across data, and interfaces with other tools. This project seeks to answer a number of fundamental questions that relate to efficient utilization of large network- structured datasets: - what are (provably) optimal storage schemes for large network structured databases? how should multiple versions of same/ related datasets be stored? how does one trade-off compression with query efficiency? and how does one suitably abstract network data so that users can interactively interrogate them using front-ends such as Cytoscape? This project aims to answer these questions by developing theoretically grounded and computationally validated storage schemes, algorithms, and software that will enable efficient and effective storage, update, processing, and querying of biological networks. We will develop compression techniques for efficient storage and version control mechanisms that allow users to create their own versions of networks, algorithms for efficient query processing on these networks, and implementations of these algorithms into broadly accessible and user-friendly software. This research will result in novel computational tools that will be disseminated to the community in the form of open source public domain software. Our tools will render network data fundamentally more accessible to the broader community in biomedical sciences. This will make use of network data more common place in applications including the identification of composite prognostic and diagnostic markers, disease gene prioritization, modeling of tumor het- erogeneity and progression in cancers, informing treatment, identification of therapeutic targets, and drug repositioning. From these points of view, the algorithms and software have far reaching and deep impact.