Conference item
ABC(SMC) 2: simultaneous inference and model checking of chemical reaction networks
- Abstract:
- We present an approach that simultaneously infers model parameters while statistically verifying properties of interest to chemical reaction networks, which we observe through data and we model as parametrised continuous-time Markov Chains. The new approach simultaneously integrates learning models from data, done by likelihood-free Bayesian inference, specifically Approximate Bayesian Computation, with formal verification over models, done by statistically model checking properties expressed as logical specifications (in CSL). The approach generates a probability (or credibility calculation) on whether a given chemical reaction network satisfies a property of interest.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, 1.3MB, Terms of use)
-
- Publisher copy:
- 10.1007/978-3-030-60327-4_14
Authors
- Publisher:
- Springer
- Host title:
- Computational Methods in Systems Biology
- Pages:
- 255-279
- Series:
- Lecture Notes in Bioinformatics
- Series number:
- 12314
- Publication date:
- 2020-09-29
- Acceptance date:
- 2020-07-01
- Event title:
- 18th Conference on Computational Methods in Systems Biology (CMSB 2020)
- Event location:
- Online
- Event website:
- https://cmsb2020.uni-saarland.de/
- Event start date:
- 2020-09-23
- Event end date:
- 2020-09-25
- DOI:
- EISSN:
-
2366-6331
- EISBN:
- 9783030603274
- ISBN:
- 9783030603267
- Language:
-
English
- Keywords:
- Pubs id:
-
1129745
- Local pid:
-
pubs:1129745
- Deposit date:
-
2020-09-02
Terms of use
- Copyright holder:
- Springer Nature Switzerland AG
- Copyright date:
- 2020
- Rights statement:
- © Springer Nature Switzerland AG 2020
- Notes:
- This conference paper was presented at the 18th Conference on Computational Methods in Systems Biology (CMSB 2020), September 23–25, 2020, online. This is the accepted manuscript version of the paper. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-60327-4_14
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