Mitra ED, Hlavacek WS. Bayesian inference using qualitative observations of underlying continuous variables.
Bioinformatics 2020;
36:3177-3184. [PMID:
32049328 PMCID:
PMC7214020 DOI:
10.1093/bioinformatics/btaa084]
[Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 01/08/2020] [Accepted: 02/03/2020] [Indexed: 01/28/2023] Open
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 observations of underlying continuous variables or a combination of qualitative and quantitative data. To demonstrate the resulting UQ capabilities, we analyzed a published model for immunoglobulin E (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 AND IMPLEMENTATION
The likelihood functions presented here are implemented in a new release of PyBioNetFit, an open-source application for analyzing Systems Biology Markup Language- and BioNetGen Language-formatted models, available online at www.github.com/lanl/PyBNF.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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