Bayesian inference and model choice in a hidden stochastic two-compartment model of hematopoietic stem cell fate decisions

Details Bayesian inference and model choice in a hidden stochastic two-compartment model of hematopoietic stem cell fate decisions Bayesian inference and model choice in a hidden stochastic two-compartment model of hematopoietic stem cell fate decisions

2010-11-02

arxiv.org/abs/1011.0601v1

Annals of Applied Statistics 2009, Vol. 3, No. 4, 1695-1709

Statistics

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Published in at http://dx.doi.org/10.1214/09-AOAS269 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS269

Despite rapid advances in experimental cell biology, the in vivo behavior of hematopoietic stem cells (HSC) cannot be directly observed and measured. Previously we modeled feline hematopoiesis using a two-compartment hidden Markov process that had birth and emigration events in the first compartment. Here we perform Bayesian statistical inference on models which contain two additional events in the first compartment in order to determine if HSC fate decisions are linked to cell division or occur independently. Pareto Optimal Model Assessment approach is used to cross check the estimates from Bayesian inference. Our results show that HSC must divide symmetrically (i.e., produce two HSC daughter cells) in order to maintain hematopoiesis. We then demonstrate that the augmented model that adds asymmetric division events provides a better fit to the competitive transplantation data, and we thus provide evidence that HSC fate determination in vivo occurs both in association with cell division and at a separate point in time. Last we show that assuming each cat has a unique set of parameters leads to either a significant decrease or a nonsignificant increase in model fit, suggesting that the kinetic parameters for HSC are not unique attributes of individual animals, but shared within a species.

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