Network modeling of gene expression data

  • 国科大
  • 日期:2010-05-19
  • 2519
王栩菁教授 (Medical College of Wisconsin)
  ※ 1989-1995年Texas A&M University, 理论物理学博士,
  ※ 1995-1999年University of Texas M.D. Anderson Cancer Center工作,
  ※ 现为 Max McGee National Research Center助教授,
  ※ 1997年荣获 Theodore Law UCF Scientific Achievement Fellowship。
内容摘要:The importance of the network structure underlies genes and proteins is gaining increasing appreciation. This is not only fundamental to the understanding of genetic regulation and its functional structure, but also critical to dissect complex diseases. Time series gene expression data offer a rich source for network inference. Here we report some progress that we have made in this field. We adopt the dynamic Bayesian network (DBYN) approach, and developed new algorithms to incorporate existing genetic information (co-citation, GO similarity, positional and binding information, etc) in public databases as prior knowledge. Further we defined gene expression synchronization module and utilized it to assist initial network structure construction. We show that this lead to significantly improved performance. We have recently integrated such works with the genetic study of complex disease, specifically, the investigation of the genetic mechanism underlying the age-at-onset heterogeneity in type 1 diabetes. We examined the network structure of the candidate disease pathways that we have identified through mathematical modeling, and prioritize the candidate genes according to their role in the network. Genes interact with more genes, have a regulatory role over others, were given higher priority. We have recently genotyped the one SNP marker each of four top candidate genes that we have identified, and have obtained have yield highly suggestive p-values in three of the four.