Bayesian inference and model choice in a hidden stochastic two-compartment model of hematopoietic stem cell fate decisions
Youyi Fong, Peter Guttorp, Janis Abkowitz

TL;DR
This study uses Bayesian inference to analyze a hidden Markov model of hematopoietic stem cell behavior, revealing that stem cell fate decisions involve both division-linked and independent processes, with shared parameters across individuals.
Contribution
It introduces an extended two-compartment model with additional events and demonstrates its improved fit to data, providing new insights into HSC fate determination mechanisms.
Findings
HSC must divide symmetrically to maintain hematopoiesis
Augmented model with asymmetric division fits data better
Kinetic parameters are shared within a species, not individual-specific
Abstract
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…
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