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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F23%3A00134426" target="_blank" >RIV/00216224:14330/23:00134426 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1371/journal.pone.0291151" target="_blank" >https://doi.org/10.1371/journal.pone.0291151</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1371/journal.pone.0291151" target="_blank" >10.1371/journal.pone.0291151</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA22-10845S" target="_blank" >GA22-10845S: Unraveling the role of polyhydroxyalkanoates in Schlegelella thermodepolymerans – promising environmental bacterium for next generation biotechnology</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Plos one

ISSN

1932-6203

e-ISSN

—

Volume of the periodical

18

Issue of the periodical within the volume

11

Country of publishing house

US - UNITED STATES

Number of pages

26

Pages from-to

1-26

UT code for WoS article

001125277400020

EID of the result in the Scopus database

2-s2.0-85176771172