植物基因型与表型间复杂的相互关系涉及到细胞内多种组分间的时空调控和代谢产物在不同细胞器或组织中的分布，一直是研究人员关注的热点和难点。本项目以水稻为研究对象，从水稻基因组注释信息出发，拟利用一个半自动化的流程对现有生物学公共数据库的信息进行整合，完成水稻基因组尺度的代谢网络的初步构建，并通过系统生物学的方法和实验手段对网络进行精细的修正和评估。在此基础上，整合蛋白互作网络、转录调控网络的实验验证数据与预测数据，完成水稻多层次参考调控网络的初步构建。结合水稻各组织包括根、茎、叶等在生长过程中的基因表达谱数据，利用动态贝叶斯模型，重构水稻生长过程中多层次基因调控网络。本研究将为水稻生物分子间调控机制的研究提供参考，系统促进水稻基因型与表现型间关系的分子机理研究。

Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology, which ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways (GSMM). Integration of regulatory information on different levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. In this proposal, we firstly use the procedure previously applied to the genome-scale metabolic reconstruction of Arabidopsis to integrate the gene-enzyme-reaction associated information. Consequently, the draft metabolic network were carefully curated by system biology and experimental methods, including charge calculation, reaction balancing, reaction reversibility prediction and gap filling and so on. Furthermore, genome-wide protein-protein interactions, gene regulations were integrated into the metabolic model. Eventually, the gene expression profiling of different tissues of rice during growth process were used to construct the tissue specific multiple-level gene regulatory network based on dynamic Bayes model. According to the “part-whole” theory proposed by West GB et al., the dynamic multiple-level gene regulatory network during rice growth process will be built via integration of the tissue-specific gene regulatory network as a whole model. Finally, a database was developed for systematically storing and retrieving the genome-scale multi-level network of rice to facilitate biomolecular regulatory analysis and gene-metabolite mapping. A visualization platform will be developed for individual gene-centric multiple level networks visualization in three dimensions (3D). The multiple-level regulatory information obtained in this study will be crucial for interpreting the rice genome interactome and understanding the genotype-phenotype relationship of rice.