Theoretical Foundations and Software Infrastructure for Biological Network Databases

 描述（由申请人提供）：生物分子（包括DNA调控区、功能性RNA、蛋白质、代谢物、脂质以及基因组变体）之间的物理、功能和统计相互作用数据量不断增加，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的方法，导致生物医学科学的独特进步。然而，不幸的是，与其他“at”或低维数据表示（例如，序列或分子表达）。当我们考虑到细胞中可能发生的相互作用的异质性时，这种复杂性就更加明显了。例如，一对蛋白质编码基因可以以多种方式相互作用：i）我们可以模拟它们的基因产物之间的物理相互作用，或它们的蛋白质-蛋白质相互作用，ii）基因产物与另一个基因的启动子/增强子/沉默子区域的相互作用，或iii）具有与单突变组合的效果显著不同的表型的双突变体之间的遗传相互作用。当人们考虑不同版本的数据集、用于分析和收集分子之间相互作用的不同技术、数据之间的联系以及与其他工具的接口时，这种复杂性就更加明显了。 该项目旨在回答一些与有效利用大型网络结构数据集有关的基本问题： 对于大型网络结构化数据库，什么是（可证明的）最佳存储方案？如何存储相同/相关数据集的多个版本？如何在压缩与查询效率之间进行权衡？如何适当地抽象网络数据，以便用户可以使用前端（如Cytoscape）交互式地询问它们？该项目旨在通过开发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，这些方案，算法和软件将实现生物网络的高效存储，更新，处理和查询。我们将开发高效存储和版本控制机制的压缩技术，允许用户创建自己的网络版本，在这些网络上进行高效查询处理的算法，以及将这些算法实现为广泛访问和用户友好的软件。 这项研究将产生新的计算工具，并以开源公共领域软件的形式向社区传播。我们的工具将使网络数据从根本上更容易被生物医学科学的更广泛社区所访问。这将使网络数据的使用在应用中更常见，包括复合预后和诊断标志物的鉴定、疾病基因优先化、癌症中肿瘤异质性和进展的建模、通知治疗、治疗靶点的鉴定和药物重新定位。从这些角度来看，算法和软件具有深远而深刻的影响。

 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.