An Integrated Approach to Funtional Genomics and Bioinformatics in a Model Legume

蒺藜苜蓿（Medicago truncatula）是世界上最重要的豆科牧草苜蓿（Medicago sativa）的近亲。它是天然产物的丰富来源，如黄酮类化合物，黄酮类化合物和三萜类化合物，这些影响其作为饲料豆类的特性。该项目的主要实验方法是通过将细胞培养物暴露于生物和非生物激发子来干扰这些天然产物的表达和代谢的其他领域。使用细胞悬浮培养物将允许收集足够的材料并进行平行分析。选择了三种模拟自然环境挑战的实验条件。该项目的最终目标是为控制和诱导细胞培养产生真正功能性的基因组学数据集。这些数据将包括表达的序列信息和相关的mRNA、蛋白质和代谢物的身份和浓度。该项目将产生各种数据，因此必须建立综合模型和软件，以促进数据之间的关系分析以及对序列和途径的先前知识的分析。当软件使用户能够以动态和透明的方式“导航”生物数据时，软件是发现过程的促进者，只需要最基本的计算技能。该项目的生物信息学部分将：一）建立一个关系数据库，以存储所有数据;二）建立一个可扩展的分析服务器，以便于用几种统计和数值算法处理数据;三）通过网络界面整合上述组成部分。数据和软件将公开提供，该项目产生的数据将用于构建诱导后时间进程的定量预测模型，这是解释潜在的复杂生物过程的调控所必需的。这些数据将提供有关在转录、翻译和代谢水平上响应生物和非生物信号的基因表达重编程的程度和性质的信息。在涉及植物天然产物的重要农艺性状的定向基因发现中也将有实际应用。最后，该项目将向科学界提供一个能够支持从转录组到代谢组的功能基因组学的生物信息学系统。

Medicago truncatula is a close relative of the world's most important forage legume, alfalfa (Medicago sativa). It is a rich source of natural products, such as flavonoids, isoflavonoids and triterpenes, which impact its properties as a forage legume. The main experimental approach of this project is to perturb the expression of these natural products, and other areas of metabolism, by exposing cell cultures to biotic and abiotic elicitors. Use of cell suspension cultures will allow sufficient material to be collected and analyzed in parallel. Three experimental conditions have been chosen that mimic natural environmental challenges. The ultimate goal of this project is to generate a truly functional genomics data set for control and elicited cell cultures. Such data will encompass expressed sequence information and the associated mRNA, protein and metabolite identities and concentrations. This project will produce a variety of data so it becomes imperative to establish integrative models and software to facilitate relational analysis of the data to each other and to previous knowledge on sequences and pathways. Software is a facilitator of the discovery process when it enables the user to "navigate" the biological data in a dynamic and transparent way, requiring only the most basic computational skills. The bioinformatics component of this project will: i) construct a relational database to store all data; ii) construct an expandable analysis server that will facilitate processing the data with several statistical and numerical algorithms; and iii) integrate the above components through a web interface. The data as well as the software will be made available publicly.The data generated by this project will be used to construct a quantitative predictive model of the time courses after elicitation, which is required to interpret the regulation of the underlying complex biological processes. The data will provide information about the extent and nature of gene expression reprogramming in response to biotic and abiotic signals at the transcription, translation and metabolic levels. There will also be practical applications in directed gene discovery for important agronomic traits involving plant natural products. Finally, this project will make available to the scientific community a bioinformatics system capable of supporting functional genomics ranging from the transcriptome to the metabolome.