Bayesian Uncertainty Quantification for Systems Biology Models Parameterized Using Qualitative Data
| Autor: | Mitra, Eshan D., Hlavacek, William S. |
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| Rok vydání: | 2019 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: 19 pages, 4 figures, 11 supplementary figures |
| Databáze: | arXiv |
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