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This is an old revision of JiguangWang made by JiguangWang on 2011-05-17 20:38:21.

 

Jiguangwang2 ==中文==

....

Ji-Guang Wang



www.amss.ac.cn Academy of Mathematics and Systems Science
Graduate University of Chinese Academy of Science, China


Institute of Applied Mathematic
Academy of Mathematics and Systems Science
Chinese Academy of Science








Educational Background



Research Areas



PhD thesis


Titile: The Research on Molecular Network Models of Complex Disease
Abstract: Complex diseases such as cardiovascular disease, cancer and diabetes are major killers of human health. Unlike single gene defect diseases, complex diseases are usually believed to be associated with the interactions among genes and the interactions among genes and environmental factors. The underlying complex pathogeneses make both the early diagnosis and treatment be difficult. Therefore the research of complex diseases is one of the major challenges of biomedical research in this century. Recently, the rapid accumulation of biological knowledge and multi-level high-throughput omics data, revolutionary changed the research paradigm on complex disease. Instead of only focusing on single molecular, researchers are gradually extending their research to systematically analyze genome-wide biomolecular interactions, i.e. biomolecular networks. In this context, bio-molecular network, a powerful tool to study complex diseases, enables systematically integration of high throughput biological data and plenty of biological knowledge. Recently, a large number of biological networks have been constructed, including protein interaction network, transcriptional regulation network, signal transduction network, metabolic network and so on. These studies have played a significant role in revealing the pathogenesis of complex diseases and promoting treatment or prevention of diseases. However, the biomolecular network research on complex disease is still in its infancy. Due to the complexity of mechanisms and disease-related biological data, many challenges remain to be further explored and resolved. In this thesis, we use mathematical models of biomolecular networks to address some main challenges in the research of complex diseases. Specifically, we study molecular networks step by step from static, node dynamic, edge dynamic, to module dynamic of network construction and network analysis, and build several mathematical models to deepen the complex disease study by biomolecular networks concept. The main results of this thesis are summarized as follows.

%\begin{enumerate}

1. We construct a molecular network model called Disease-Aging Network, and find (1) comparing with random control, aging and disease are significant overlap at the molecular and network level; (2) Disease can be divided into two types according to their relationship with aging: aging-related disease and non-aging-related disease. Genes related to the two disease types are different in function, evolution, and importance, etc. (3) Aging genes make significant contribution in connecting disease genes

2. We propose a Gaussian Graphical Model to construct the resolution limit
and ``misidentification. Here, we construct a constrained optimization model to address the above mentioned limitations, and solve the model by a revised simulated annealing-based algorithm. Then we apply the new model in the research of mouse teratoma, and explain the observed phenotype by a dynamic module model. Furthermore, the new method is developed as a free accessible software at \url{http://www.aporc.org/doc/wiki/ModularityOptimization}

5. We develop a Network Ontology Analysis model to analyze the function of biological networks, which is important to connect the complex disease phenotypes with biomolecular networks. Current tools for the analysis of biological networks are limited to analyze a set of genes or proteins involved in the network, and ignore the function of links and network topology. We construct a multi-objective programming model to define the function of edges in biological networks, and then use statistical tests to assess the enrichment GO terms for a given biological network. The new method is proved to be more efficient than traditional ones in both static and dynamic networks. We further apply it to the aging network, cancer network, and Alzheimer's disease networks, and find several important implications in related disease research. Furthermore, we develop a free accessible web-server for NOA at \url{http://www.aporc.org/noa/}.


Award and Honors


  • “Three goods” Student in AMSS, 2007
  • Beijing Outstanding Graduates, 2006
  • Honorable Prize in Mathematical Contest in Modeling, 2005
  • Second class Prize in CUMCM, 2005
  • Second class Prize in CUMCM, 2004
  • First class Prize in Chinese Mathematical Olympiad in Senior, 2002

Interests


  • Sports, such as basketball, ping-pang, and so on
  • Reading science fiction

Publications


  1. Ji-Guang Wang, Qiang Huang, Zhi-Ping Liu, Yong Wang, Ling-Yun Wu, Luonan Chen, Xiang-Sun Zhang,
    NOA: a novel Network Ontology Analysis method.
    Nucleic Acids Research, doi: 10.1093/nar/gkr251, 2011. ::Webserver for analyzing the function of biological networks::

  1. Yongcui Wang, Ji-Guang Wang, Zhixia Yang, Naiyang Deng,
    Sequence-based protein-protein interaction prediction via support vector machine. ::PDF download::
    Jrl Syst Sci & Complexity, 23: 1012-1023, 2010.

  1. Ji-Guang Wang, Yong Wang, Xianwen Ren, Wei Guo, Luonan Chen
    Uncover the Transient Transcriptional Regulations by a Novel Sliding Window Correlation Strategy.
    ISB, 2010. (Conference).

  1. Ji-Guang Wang, Shihua Zhang, Yong Wang, Luonan Chen, and Xiang-Sun Zhang.
    Disease-aging network reveals significant roles of aging genes in connecting genetic diseases. ::PDF download::
    PLoS Comput Biol, 5(9): e1000521. doi:10.1371/journal.pcbi.1000521, 2009.

  1. Xiang-Sun Zhang, Rui-Sheng Wang, Yong Wang, Ji-Guang Wang, Yu-Qing Qiu, Lin Wang, and Luonan Chen.
    Modularity optimization in community detection of complex networks. ::PDF download::
    Europhysics Letters, 87: 38002, 2009.

  1. Ji-Guang Wang, Lin Wang, Yu-Qing Qiu, Yong Wang, Xiang-Sun Zhang,
    A Constrained Optimization Method for Community Detection. ::Supplementary Materials::
    The Third International Symposium Proceedings, OSB'09 Zhangjiajie, China, September 20-22, 2009 .

  1. Ji-Guang Wang, Yuqing Qiu, Rui-Sheng Wang, Xiang-Sun Zhang.
    Remarks on network community properties. ::PDF download::
    Jrl Syst Sci & Complexity, 21: 637-644, 2008.

  1. Lin Wang, Yuqing Qiu, Ji-Guang Wang, Xiang-Sun Zhang.
    Recognition of structure similarities in proteins. ::PDF download::.
    Jrl Syst Sci & Complexity, 21: 665-675, 2008.



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