Bayesian Uncertainty Quantification for Systems Biology Models Parameterized Using Qualitative Data

TL;DR

This paper develops Bayesian methods for uncertainty quantification in systems biology models parameterized with qualitative data, enabling effective analysis despite limited numerical information.

Contribution

It introduces likelihood functions for Bayesian UQ with qualitative data and demonstrates their effectiveness on a signaling model, matching quantitative data results.

Findings

Qualitative data can produce parameter estimates nearly as accurate as quantitative data.

Likelihood functions enable Bayesian UQ with mixed qualitative and quantitative data.

The methods are implemented in the open-source PyBioNetFit tool.

Abstract

Motivation: Recent work has demonstrated the feasibility of using non-numerical, qualitative data to parameterize mathematical models. However, uncertainty quantification (UQ) of such parameterized models has remained challenging because of a lack of a statistical interpretation of the objective functions used in optimization. Results: We formulated likelihood functions suitable for performing Bayesian UQ using qualitative data or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for IgE receptor signaling using synthetic qualitative and quantitative datasets. Remarkably, estimates of parameter values derived from the qualitative data were nearly as consistent with the assumed ground-truth parameter values as estimates derived from the lower throughput quantitative data. These results provide further motivation for leveraging qualitative data in biological modeling. Availability: The likelihood functions presented here are implemented in a new release of PyBioNetFit, an open-source application for analyzing SBML- and BNGL-formatted models, available online at www.github.com/lanl/PyBNF.