Artikel,
Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings
Nature Methods, 11 (2): 197--202 (12.01.2014)
DOI: 10.1038/nmeth.2794
Zusammenfassung
Mathematical methods together with measurements of single-cell dynamics
provide unprecedented means to reconstruct intracellular processes that are
only partly or indirectly accessible experimentally. To obtain reliable
reconstructions the pooling of measurements from several cells of a clonal
population is mandatory. The population's considerable cell-to-cell variability
originating from diverse sources poses novel computational challenges for
process reconstruction. We introduce an exact Bayesian inference framework that
properly accounts for the population heterogeneity but also retains scalability
with respect to the number of pooled cells. The key ingredient is a stochastic
process that captures the heterogeneous kinetics of a population. The method
allows to infer inaccessible molecular states, kinetic parameters, compute
Bayes factors and to dissect intrinsic, extrinsic and technical contributions
to the variability in the data. We also show how additional single-cell
readouts such as morphological features can be included into the analysis. We
then reconstruct the expression dynamics of a gene under an inducible GAL1
promoter in yeast from time-lapse microscopy data. Based on Bayesian model
selection the data yields no evidence of a refractory period for this promoter.
