Abstracts Keynote & Plenary

Guowen Xie 1
, Meizhen Lin 2
, Yisheng Zheng 1
, Yanlin Zheng 1
, Xiaoyu Peng 1
1. School of Life Science, Guangzhou University, Guangdong, Guangzhou 510006,
China;
2. College of Geographical Science, Guangzhou University, Guangdong, Guangzhou 510006,
China
Monimopetalum chinense (Celastraceae) is a rare, endemic and clonal plant in China. In the present
study, genetic differentiation and clonal diversity of Monimopetalum chinense populations were
studied using inter simple sequence repeat ( ISSR ) markers. According to the results, the effective
conservation measures were produced. The results indicated that the population has high clonal
diversity ( D = 0.9969 ). In addition, there was great clonal differentiation among populations with no
the widespread genotyes. The level of genetic variation at species level or at population level are all
lower( species level: PPB = 39.2%, HT = 0.129; Populations level: PPB = 15.64%, HS = 0.0557 )than
that most of clonal plant. Strong genetic differentiation among populations was detected in
Monimopetalum chinense, with 49.06% of total genetic variation ( AMOVA ), 0.5672 of Nei's gene
differentiation coefficient ( GST ) and 0.5464 of Shannon information index. The population
fragmentation since the end of Tertiary period, genetic drift of small population size and the form of
inbreeding reproduction would have contributed to such genetic differentiation among populations of
Monimopetalum chinense. Based on these findings, it was suggested that these populations with
relatively high genetic diversity should be protected in situ, and adopted introduction among
populations for this species, so that the genetic diversity of Monimopetalum chinense should be
conservated effectively.
Key words: Monimopetalum
chinense, population, ISSR, genetic differentiation, clonal diversity
PO-015
Using Adaptive
K-nearest Neighbor Algorithm and Cellular Automata Images to Predicting
G-Protein-Coupled Receptor Classes
Xuan Xiao and Wang-ren Qiu
Computer Department , Jing-De-Zhen
Ceramic Institute, Jing-De-Zhen 33300, China
G-Protein-Coupled Receptor (GPCRs) are the largest of cell surface receptor, accounting
for >1% of
the human genome. They play a key role in cellular signaling networks that regulate various
physiological processes. The functions of many of GPCRs are unknown, because they are difficult to
crystallize and most of them will not dissolve in normal solvents. This difficulty has motivated and
challenged the development of a computational method which can predict the classification of the
families and subfamilies of GPCRs based on their primary sequence so as to help us classify drugs. In
this paper the adaptive K-nearest neighbor algorithm and protein cellular automata image (CAI) is
introduced. Based on the CAI, the complexity measure factors derived from each of the protein
sequences concerned are adopted for its Pseudo amino acid composition. GPCRs were categorized into
nine subtypes. The overall success rate in identifying GPCRs among their nine family classes was
about 83.5%. The high success rate suggests that the adaptive k-nearest neighbor algorithm and protein
CAI holds very high potential to become a useful tool for understanding the actions of drugs that target
GPCRs and designing new medications with fewer side effects and greater efficacy
PO-016
Evaluation of Coupled Nuclear and Cytoplasmic p53 Dynamics
Tingzhe Sun, Jun Cui, Meihong Cai and Pingping Shen*
State Key Laboratory of Pharmaceutical Biotechnology, School
of Life Sciences, Nanjing University,
Nanjing, 210093, People’s Republic of China.