title: Parameter estimation for an immortal model of colonic stem cell division using approximate Bayesian computation.
abstract: In this paper we use approximate Bayesian computation to estimate the parameters in an immortal model of colonic stem cell division. We base the inferences on the observed DNA methylation patterns of cells sampled from the human colon. Utilising DNA methylation patterns as a form of molecular clock is an emerging area of research and has been used in several studies investigating colonic stem cell turnover. There is much debate concerning the two competing models of stem cell turnover: the symmetric (immortal) and asymmetric models. Early simulation studies concluded that the observed methylation data were not consistent with the immortal model. A later modified version of the immortal model that included preferential strand segregation was subsequently shown to be consistent with the same methylation data. Most of this earlier work assumes site independent methylation models that do not take account of the known processivity of methyltransferases whilst other work does not take into account the methylation errors that occur in differentiated cells. This paper addresses both of these issues for the immortal model and demonstrates that approximate Bayesian computation provides accurate estimates of the parameters in this neighbour-dependent model of methylation error rates. The results indicate that if colonic stem cells divide asymmetrically then colon stem cell niches are maintained by more than 8 stem cells. Results also indicate the possibility of preferential strand segregation and provide clear evidence against a site-independent model for methylation errors. In addition, algebraic expressions for some of the summary statistics used in the approximate Bayesian computation (that allow for the additional variation arising from cell division in differentiated cells) are derived and their utility discussed.
authors.affiliation: School of Mathematics and Statistics, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, United Kingdom. k.walters@sheffield.ac.uk
authors.forename: Kevin
authors.initials: K
authors.lastname: Walters
mesh_terms: D000328:Adult; D000368:Aged; D001499:Bayes Theorem; D002455:Cell Division / Q000502:physiology; D019070:Cell Lineage / Q000502:physiology; D003106:Colon / Q000166:cytology*; D019295:Computational Biology / Q000379:methods; D019175:DNA Methylation; D006801:Humans; D008954:Models, Biological; D009010:Monte Carlo Method; D013234:Stem Cells / Q000166:cytology*