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Motivation: Recent work has demonstrated the feasibility of usingnon-numerical, qualitative data to parameterize mathematical models. However,uncertainty quantification (UQ) of such parameterized models has remainedchallenging because of a lack of a statistical interpretation of the objectivefunctions used in optimization. Results: We formulated likelihood functionssuitable for performing Bayesian UQ using qualitative data or a combination ofqualitative and quantitative data. To demonstrate the resulting UQcapabilities, we analyzed a published model for IgE receptor signaling usingsynthetic qualitative and quantitative datasets. Remarkably, estimates ofparameter values derived from the qualitative data were nearly as consistentwith the assumed ground-truth parameter values as estimates derived from thelower throughput quantitative data. These results provide further motivationfor leveraging qualitative data in biological modeling. Availability: Thelikelihood functions presented here are implemented in a new release ofPyBioNetFit, an open-source application for analyzing SBML- and BNGL-formattedmodels, available online at www.github.com/lanl/PyBNF.

Bayesian Uncertainty Quantification for Systems Biology Models Parameterized Using Qualitative Data