Comparing chemical reaction networks: A categorical and algorithmic perspective
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2046
- Type
- D - Journal article
- DOI
-
10.1016/j.tcs.2017.12.018
- Title of journal
- Theoretical Computer Science
- Article number
- -
- First page
- 47
- Volume
- 765
- Issue
- -
- ISSN
- 0304-3975
- Open access status
- Exception within 3 months of publication
- Month of publication
- December
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- We study chemical reaction networks (CRNs), a common modelling framework in the sciences, seen as a model of concurrency provided with semantics based on ordinary differential equations. We investigate the problem of comparing two CRNs, i.e., to decide whether the solutions of a source and of a target CRN can be matched for an appropriate choice of initial conditions. Using a categorical framework, we extend and unify model-comparison approaches based on dynamical (semantic) and structural (syntactic) properties of CRNs. We provide an algorithm and tool to compare CRNs, and we apply our results to biological models from the literature.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -