Assessment of prediction uncertainty quantification methods in systems biology
| Autor: | Alejandro F. Villaverde, Elba Raimundez, Jan Hasenauer, Julio R. Banga |
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| Přispěvatelé: | European Commission, German Research Foundation, Xunta de Galicia, Agencia Estatal de Investigación (España), Banga, Julio R. |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Mathematical models
Observability 12 Matemáticas Applied Mathematics Biological system modeling Data models Prediction error methods Uncertainty Promote peaceful and inclusive societies for sustainable development provide access to justice for all and build effective accountable and inclusive institutions at all levels Computational modeling 2404 Biomatemáticas Dynamic models Predictive models Genetics Computational methods Nonlinear systems Uncertain systems State estimation Biotechnology |
| Zdroj: | IEEE/ACM Trans. Comput. Biol. Bioinform., DOI: 10.1109/TCBB.2022.3213914 (2022) |
| DOI: | 10.1109/TCBB.2022.3213914 |
| Popis: | Biological processes are often modelled using ordinary differential equations. The unknown parameters of these models are estimated by optimizing the fit of model simulation and experimental data. The resulting parameter estimates inevitably possess some degree of uncertainty. In practical applications it is important to quantify these parameter uncertainties as well as the resulting prediction uncertainty, which are uncertainties of potentially time-dependent model characteristics. Unfortunately, estimating prediction uncertainties accurately is nontrivial, due to the nonlinear dependence of model characteristics on parameters. While a number of numerical approaches have been proposed for this task, their strengths and weaknesses have not been systematically assessed yet. To fill this knowledge gap, we apply four state of the art methods for uncertainty quantification to four case studies of different computational complexities. This reveals the trade-offs between their applicability and their statistical interpretability. Our results provide guidelines for choosing the most appropriate technique for a given problem and applying it successfully. This research has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 686282 (CanPathPro). ER and JH acknowledge funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - TRR333/1 - 450149205 and under Germany’s Excellence Strategy EXC-2047/1 - 390685813 and EXC 2151 - 390873048.AFV acknowledges funding from grant ED431F 2021/003 Universitaria, Xunta de Galicia; and from grant RYC-2019- 027537-I funded by MCIN/AEI/ 10.13039/501100011033 and by “ESF Investing in your future”. JRB acknowledges funding from MCIN/AEI/ 10.13039/501100011033 through grant PID2020-117271RB-C22 (BIODYNAMICS) |
| Databáze: | OpenAIRE |
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