ArticleBiology direct2010
A new stochastic and state space model of human colon cancer incorporating multiple pathways.
Article in Biology direct, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Quantifying rate-limiting genetic variation in breast and ovarian tumourigenesis.EBioMedicine · 2026Article
- Deletions Rate-Limit Breast and Ovarian Cancer Initiation.bioRxiv : the preprint server for biology · 2024Article
- Mathematical modelling the pathway of genomic instability in lung cancer.Scientific reports · 2019Article
- Evaluating intrinsic and non-intrinsic cancer risk factors.Nature communications · 2018Review
- A mechanistic model for atherosclerosis and its application to the cohort of Mayak workers.PloS one · 2017Article
- Article
- Colorectal cancer through simulation and experiment.IET systems biology · 2013Review
- New stochastic carcinogenesis model with covariates: an approach involving intracellular barrier mechanisms.Mathematical biosciences · 2012Article
- Cancer models, genomic instability and somatic cellular Darwinian evolution.Biology direct · 2010Review
- Evolution and cancer: a mathematical biology approach.Biology direct · 2010Article
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2 authors.
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Abstract
background and purposeStudies 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.
resultsBased 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.
conclusionsOur 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.
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