title: A new stochastic and state space model of human colon cancer incorporating multiple pathways.
abstract: OBJECTIVE
  Studies by molecular biologists and geneticists have shown that tumors of human colon cancer are developed from colon stem cells through two mechanisms: The chromosomal instability and the micro-satellite instability. The purpose of this paper is therefore to develop a new stochastic and state space model for carcinogenesis of human colon cancer incorporating these biological mechanisms.
  
  
  RESULTS
  Based on recent biological studies, in this paper we have developed a state space model for human colon cancer. In this state space model, the stochastic system is represented by a stochastic model, involving 2 different pathways-the chromosomal instability pathway and the micro-satellite instability pathway; the observation, cancer incidence data, is represented by a statistical model. Based on this model we have developed a generalized Bayesian approach to estimate the parameters through the posterior modes of the parameters via Gibbs sampling procedures. We have applied this model to fit and analyze the SEER data of human colon cancers from NCI/NIH.
  
  
  CONCLUSIONS
  Our results indicate that the model not only provides a logical avenue to incorporate biological information but also fits the data much better than other models including the 4-stage single pathway model. This model not only would provide more insights into human colon cancer but also would provide useful guidance for its prevention and control and for prediction of future cancer cases.
authors.affiliation: Department of Mathematical Sciences, The University of Memphis, Memphis, TN 38152-6429, USA. waitan@memphis.edu
authors.forename: Wai Y
authors.initials: WY
authors.lastname: Tan
authors.forename: Xiao W
authors.initials: XW
authors.lastname: Yan
mesh_terms: D003110:Colonic Neoplasms / Q000378:metabolism*; D006801:Humans; D015233:Models, Statistical; D015398:Signal Transduction / Q000502:physiology*; D013269:Stochastic Processes
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