CAREER: Computational Tools for Evolutionary Analysis of Biological Interaction Networks

生物技术的快速发展正在以前所未有的速度和速度积累生物相互作用数据，例如蛋白质-蛋白质和基因-基因相互作用网络，提供了一种新的强大资源，并允许在新的背景下重新制定旧的但重要的生物学问题。这些新型数据的规模和复杂性对实验生物学家和计算生物学家都提出了巨大的挑战。解决这些挑战一直是系统生物学这一总称下许多研究的主要焦点。然而，在提供基因组和相互作用数据同时进行进化分析的工具方面，几乎没有做过任何工作。该项目将划定的意义&amp；#64257；不能影响这样的同步分析可以对理解和分析生物相互作用网络，并将探索新的方法进行计算分析。特别要指出的是，有两个方面将有助于阐明交互网络及其复杂性：新的基因组-相互作用组进化模型。聚结理论一直是建立基因谱系和物种系统发生关系的核心模型之一。以目前的形式，该理论既不允许对基因组研究中出现的事件进行建模，例如基因复制和丢失，也没有被用来解释相互作用网络的进化。这项研究将扩展聚结理论来模拟基因组尺度的进化事件，并建立一个新的统一标准。为基因组和相互作用数据的同步进化建模的框架。2. 基于图语法的新型随机建模与推理。随机模型，如隐马尔可夫模型和随机上下文无关语法，在生物序列数据分析中得到了广泛的应用。然而，目前还没有引入等效模型来分析相互作用网络。这项研究将探索随机图语法的新应用，以及这些随机模型可以用来提供这些网络的深刻分析的方法。广泛的影响位于细胞生物学、分子生物学和进化生物学的交叉点，这项工作将具有重大意义。不会影响随机图语法和不相似度量等计算工具的发展和应用。该项目将为在跨学科领域培训学生提供机会，并将导致开发新的课程，重点是生物网络的进化分析。拟议工作的跨学科性质将有助于从传统上代表性不足的群体中成功招募计算机科学专业的学生。项目方法将在软件包中实施，并通过开放源代码机制提供。

Intellectual MeritRapid advances in biotechnologies are amassing biological interaction data, such as protein-protein and gene-gene interaction networks, at unprecedented pace and rate, presenting a new powerful resource and allowing the reformulation of old, yet important, biological questions in a new context. The size and complexity of these new types of data pose great challenges for experimental and computational biologists alike. Addressing these challenges has been a primary focus of much research under the umbrella term of systems biology. However, almost no work has been done on providing tools for simultaneous evolutionary analysis of genomic and interactomic data. This project will delineate the significant impact such a simultaneous analysis can have on understanding and analyzing biological interaction networks, and will explore new methodologies for conducting computational analyses. In particular, two areas will be addressed that will help shed light on interaction networks and their complexity:1. Novel genome-interactome evolutionary models. Coalescent theory has been one of the central models for establishing the relationships among gene genealogies and species phylogenies. In its current form this theory neither allows for modeling events that arise in genomic studies, such as gene duplication and loss, nor has it been used to explain interaction network evolution. This research will extend coalescent theory to model genome-scale evolutionary events, and develop a new unified framework for modeling the simultaneous evolution of genomic and interactomic data. 2. Novel stochastic modeling and inference using graph grammars. Stochastic models, such as hidden Markov models and stochastic context-free grammars, have been used extensively in the analysis of biological sequence data. However, no equivalent models have been introduced for analysis of interaction networks. This research will explore new applications of stochastic graph grammars, as well as ways in which these stochastic models can be used to provide insightful analyses of these networks. Broad ImpactSituated at the intersection of cellular, molecular, and evolutionary biology, this work will have a significant impact on the development and applications of computational tools such as stochastic graph grammars and dissimilarity measures. The project will provide opportunities for training students in an interdisciplinary area, and will result in the development of new courses focused on evolutionary analysis of biological networks. The interdisciplinary nature of the proposed work will help successfully recruit students to computer science from traditionally under-represented groups. The project methodologies will be implemented in software packages and made available through open-source mechanisms.