Pathway Prediction and Assessment Integrating Multiple Evidence Types

描述(由申请人提供)： 代谢途径数据库提供了一个生物学框架，可以在其中揭示有机体基因之间的关系。这一背景可以被用来提高基因组注释的准确性，发现治疗的新靶点，或者设计细菌中的新陈代谢途径，以廉价而快速地生产一种历史上昂贵的药物。但是，对特征不佳物种的新陈代谢知识是有限的，并依赖于对途径的计算预测。我们的最终目标是开发预测任何生物体中新的代谢途径的方法，并对任何预测的途径的有效性进行强有力的评估。我们假设，整合来自生物体代谢网络的多个层面的证据--从网络中路径的适配性到路径之间的进化关系--将使我们能够评估路径的有效性并预测新的代谢路径。我们已经成功地将机器学习方法应用于识别代谢途径中缺失的酶的问题，并相信类似的方法将在这一应用中被证明是卓有成效的。我们的初步研究已经确定了预测的代谢途径的几个特性，它们在真阳性途径预测集(即，已知发生在生物体中的途径)和假阳性途径预测集之间存在差异。我们将对这些功能进行扩展，并制定方法以实现以下具体目标： 1)在计算机生成的路径/基因组数据库中，基于对高度精选的生物(例如，大肠杆菌和拟南芥)的预测，确定在区分正确和不正确的路径预测方面具有信息量的特征。 2)发展计算路径被正确预测的概率的方法。在特定目标#1中识别的信息特征将被集成到一个分类器中，该分类器将在给定相关证据的情况下计算预测路径正确的概率。 3)扩展病理程序(用于推断生物体新陈代谢网络的路径工具算法)以预测生物体中以前未知的替代路径。我们将在MetaCyc反应空间(包括近6000个反应)中搜索新的亚途径，使用生物体特有的证据(即同源性、实验证据等)明确限制我们的搜索。在每一步。

DESCRIPTION (provided by applicant): Metabolic pathway databases provide a biological framework in which relationships among an organism's genes may be revealed. This context can be exploited to boost the accuracy of genome annotation, to discover new targets for therapeutics, or to engineer metabolic pathways in bacteria to produce a historically expensive drug cheaply and quickly. But, knowledge of metabolism in ill-characterized species is limited and dependent on computational predictions of pathways. Our ultimate target is to develop methods for the prediction of novel metabolic pathways in any organism, coupled with robust assessment of the validity of any predicted pathway. We hypothesize that integrating evidence from multiple levels of an organism's metabolic network - from the fit of a pathway within the network to evolutionary relationships between pathways - will allow us to assess pathway validity and to predict novel metabolic pathways. We have successfully applied machine learning methods to the problem of identifying missing enzymes in metabolic pathways and believe similar methods will prove fruitful in this application. Our preliminary studies have identified several properties of predicted metabolic pathways that differ between sets of true positive pathway predictions (i.e., pathways known to occur in an organism) and sets of false positive pathway predictions. We will expand on these features and develop methods to address the following specific aims: 1) Identify features that are informative in distinguishing between correct and incorrect pathway predictions in computationally-generated pathway/genome databases based on predictions for highly-curated organisms (e.g., Escherichia coli and Arabidopsis thaliana). 2) Develop methods for computing the probability that a pathway is correctly predicted. Informative features identified in Specific Aim #1 will be integrated into a classifier that will compute the probability that a predicted pathway is correct given the associated evidence. 3) Extend the Pathologic program (the Pathway Tools algorithm used to infer the metabolic network of an organism) to predict alternate, previously unknown pathways in an organism. We will search the MetaCyc reaction space (comprising almost 6000 reactions) for novel subpathways, explicitly constraining our search using organism-specific evidence (i.e., homology, experimental evidence, etc.) at each step.