PyBioNetFit and the Biological Property Specification Language

To help modelers perform parameter identification and uncertainty quantification for biological models, we developed the software PyBioNetFit.
In this repository, we provide the raw data used to generate Figure 4, demonstrating the functionality of PyBioNetFit in Bayesian uncertainty quantification. We considered a model of mast cell signaling described in: Harmon, B. et al. Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation. Sci. Rep. 7, 15586 (2017). We performed two algorithms in PyBioNetFit: random walk Metropolis MCMC and parallel tempering. For each algorithm, we provide the raw list of sampled parameter sets, which represent samples from the Bayesian posterior distribution. We also provide a list of sampled parameters in the same format for the original Bayesian uncertainty quantification we performed in Harmon et al. (2017).