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

KLEIN, Julia, Huy PHUNG, Matej HAJNAL, David ŠAFRÁNEK and Tatjana PETROV. Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data. Plos one. San Francisco: Public Library of Science, 2023, vol. 18, No 11, p. 1-26. ISSN 1932-6203. Available from: https://dx.doi.org/10.1371/journal.pone.0291151.

Basic information

• Original name: Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data
• Authors: KLEIN, Julia, Huy PHUNG, Matej HAJNAL (703 Slovakia, belonging to the institution), David ŠAFRÁNEK (203 Czech Republic, belonging to the institution) and Tatjana PETROV.
• Edition: Plos one, San Francisco, Public Library of Science, 2023, 1932-6203.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United States of America
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 3.700 in 2022
• RIV identification code: RIV/00216224:14330/23:00134426
• Organization unit: Faculty of Informatics
• Doi: http://dx.doi.org/10.1371/journal.pone.0291151
• UT WoS: 001125277400020
• Keywords in English: Bayes Theorem; Markov Chains
• Tags: International impact, Reviewed

Abstract

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.

Links

• GA22-10845S, research and development project: Name: Studium role polyhydroxyalkanoátů u bakterie Schlegelella thermodepolymerans – slibného bakteriálního kandidáta pro biotechnologie nové generace (Acronym: PHAST)

Investor: Czech Science Foundation, Unraveling the role of polyhydroxyalkanoates in Schlegelella thermodepolymerans – promising environmental bacterium for next generation biotechnology