Collaborative Research: Cross Species Analysis of Biological Systems Using Expression Data

合作研究：使用表达数据对生物系统进行跨物种分析

• 批准号: 0965316
• 负责人: Ziv Bar-Joseph
• 金额: $ 72.11万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0965316&HistoricalAwards=false
• 关键词: Collaborative; Research; Cross; Species; Analysis

Cross species analysis of biological systems using expression dataCarnegie Mellon University and the University of Pittsburgh are awarded a grant to develop and implement algorithms and software tools for the analysis of gene expression experiments that study the same biological system in multiple species. Biological systems, including the cell division process which plays a major role in development and cancer, immune response and circadian rhythm (which governs our behavior over the course of a day) are similarly activated across many different species. Indeed, much of the progress in understanding these systems was achieved by studying their behavior in model organisms. When using expression experiments, which measure the level of genes at different time points and in different conditions, to study these systems, researchers often identify hundreds, or even thousands, of genes as being expressed in each of the species. This makes it hard to analyze the resulting sets and to accurately identify genes that control key aspects of the system. In addition, our ability to correctly detect interactions among these genes is greatly reduced since many potential interactions can be used to explain the observed expression values. This joint computational-experimental project will allow us to identify a core set of genes; genes which are conserved in both sequence and expression across multiple species. These genes represent key components of the biological system, are essential to its proper maintenance and function and would be used for further studies and for initial modeling. Predictions made by our method for these systems will be validated by new biological experiments which will in turn be used to further refine the computational model leading to a computational-experimental loop. As part of the proposal we will also develop an open source software package for cross species expression analysis. We will also develop and offer a new class on the analysis and use of cross species genomics data.

使用Datacarnegie Mellon University和匹兹堡大学对生物系统进行跨物种分析，授予了开发和实施算法和软件工具的赠款，以分析基因表达实验，这些实验研究了多种物种中相同的生物系统。生物系统，包括在发育和癌症中起主要作用的细胞分裂过程，免疫反应和昼夜节律（在一天中控制我们的行为）在许多不同的物种中类似地激活。确实，理解这些系统的许多进展是通过研究它们在模型生物中的行为来实现的。当使用表达实验时，在不同时间点和不同条件下测量基因水平，为了研究这些系统时，研究人员通常会识别数百个甚至数千个基因在每个物种中表达的基因。这使得很难分析产生的集合并准确识别控制系统关键方面的基因。此外，由于许多潜在的相互作用可用于解释观察到的表达值，因此我们正确检测这些基因之间相互作用的能力大大降低了。 这个联合计算实验项目将使我们能够识别一组核心基因。在多种物种中均保守的基因。这些基因代表生物系统的关键组成部分，对于其适当的维护和功能至关重要，将用于进一步的研究和初始建模。我们的方法对这些系统做出的预测将通过新的生物学实验来验证，这些实验又将用于进一步完善计算模型，从而导致计算实验环。作为提案的一部分，我们还将开发一个用于跨物种表达分析的开源软件包。我们还将开发并提供有关跨物种基因组学数据分析和使用的新类。