Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data.

Autor: Klein J; Department of Computer and Information Sciences, University of Konstanz, Konstanz, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany., Phung H; Department of Computer and Information Sciences, University of Konstanz, Konstanz, Germany., Hajnal M; Department of Computer and Information Sciences, University of Konstanz, Konstanz, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.; Systems Biology Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic., Šafránek D; Systems Biology Laboratory, Faculty of Informatics, Masaryk University, Brno, Czech Republic., Petrov T; Department of Computer and Information Sciences, University of Konstanz, Konstanz, Germany.; Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Nov 13; Vol. 18 (11), pp. e0291151. Date of Electronic Publication: 2023 Nov 13 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0291151
Abstrakt: Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial number of possible states of the population. Moreover, while the modeller easily hypothesises the mechanistic aspects of the model, the quantitative parameters associated to these mechanistic transitions are difficult or impossible to measure directly. In this paper, we investigate how formal verification methods can aid parameter inference for population discrete-time Markov chains in a scenario where only a limited sample of population-level data measurements-sample distributions among terminal states-are available. We first discuss the parameter identifiability and uncertainty quantification in this setup, as well as how the existing techniques of formal parameter synthesis and Bayesian inference apply. Then, we propose and implement four different methods, three of which incorporate formal parameter synthesis as a pre-computation step. We empirically evaluate the performance of the proposed methods over four representative case studies. We find that our proposed methods incorporating formal parameter synthesis as a pre-computation step allow us to significantly enhance the accuracy, precision, and scalability of inference. Specifically, in the case of unidentifiable parameters, we accurately capture the subspace of parameters which is data-compliant at a desired confidence level.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Klein et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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