Reparametrizing the Sigmoid Model of Gene Regulation for Bayesian Inference
This poster describes a novel reparametrization of a fre-quently used non-linear ordinary differential equation (ODE) model of gene regulation. We show that in its commonly used form, the model cannot reliably distinguish between both quantitatively and qualitatively different parameter combinations. The proposed reparametrization makes inference over the model stable and amenable to fully Bayesian treatment with state of the art Hamiltonian Monte Carlo methods.\n\nComplete source code and a more detailed explanation of the model is available at https://github.com/cas-bioinf/genexpi-stan.
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