Bayesian inference for model selection: an application to aberrant signalling pathways in chronic myeloid leukaemia

In the analysis of any data using statistical modelling, it is imperative that the choice of model is informed by expert knowledge and that its adequacy is de- termined based on the extent to which it captures and describes the patterns observed in the data. This is especially true in systems where a subset of the con- stituent components may not be known or cannot be observed. In this chapter, we demonstrate how statistical inference can be used to inform model selection and, by identifying where existing models are unable to sufficiently capture observed behaviour, that statistical inference can help indicate which model refinements may be required. In this chapter, we use Bayesian statistical methodology?specifically, Rie- mannian manifold population MCMC?to model interactions between molecu- lar species in the JAK/STAT pathway in chronic myeloid leukaemia (CML) and compare two candidate models. We set out the biological context for this infer- ence in Sections 1.2-1.5 and describe the two candidate models in Section 1.7. With the biology established, we describe our statistical methodology (Section 1.8) which we successfully apply in a simulation study to provide a proof of con- cept (Section 1.9), before we consider a subsequent, more biologically realistic dataset (Section 1.10) to assess which model best describes the behaviour ob- served in vitro. We relate the findings from this second synthetic study back to our model and dataset construction, thereby highlighting what further in vitro and in silico work is required (Section 1.11).