摘要
代谢网络的研究是当今生物学研究中的新热点,使用图形聚类算法对代谢网络进行分析研究是一个有力的手段.文中提出了用图形聚类方法挖掘复杂代谢网络中蕴含的功能信息并进行模块相似性分析的方法,从系统生物学角度揭示光合作用的进化.介绍了马尔可夫聚类算法和模拟退火聚类算法两种图形聚类方法;给出了两种方法对叶绿体和蓝细菌的代谢网络进行聚类的结果;定义了模块相似度评价指标,挖掘叶绿体和蓝细菌间保守的功能模块,并对其网络结构进行了比较分析.
This paper proposes a new methodology to disclose the functional information of complex metabolic network based on graph clustering algorithms and modular structure comparison, and indicates photosynthesis evolution in view of systems biology. In addition, two graph clustering algorithms, the Markov clustering and simulated annealing clustering, are introduced. The corresponding clustering results of chloroplast and cyanobacteria are presented. A similarity measure is defined to find the conservative functional module between chloroplast and cyanobacteria, and the overall structure of modules is compared with the counterparts in different soecies.
出处
《应用科学学报》
CAS
CSCD
北大核心
2006年第6期588-592,共5页
Journal of Applied Sciences
基金
国家"973"重点基础研究发展计划(2002CB713807
2004CB518606
2003CB715900)
上海市科委重大基础(04DZ14003)资助项目
