Organisation-Oriented Coarse Graining and Refinement of Stochastic Reaction Networks

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • Teesside University
  • Friedrich-Schiller University Jena, Germany

Abstract

Chemical organisation theory is a framework developed to simplify the analysis of long-term behaviour of chemical systems. In this work, we build on these ideas to develop novel techniques for formal quantitative analysis of chemical reaction networks, using discrete stochastic models represented as continuous-time Markov chains. We propose methods to identify organisations, and to study quantitative properties regarding movements between these organisations. We then construct and formalise a coarse-grained Markov chain model of hierarchic organisations for a given reaction network, which can be used to approximate the behaviour of the original reaction network. As an application of the coarse-grained model, we predict the behaviour of the reaction network systems over time via the master equation. Experiments show that our predictions can mimic the main pattern of the concrete behaviour in the long run, but the precision varies for different models and reaction rule rates. Finally, we propose an algorithm to selectively refine the coarse-grained models and show experiments demonstrating that the precision of the prediction has been improved.

Details

Original languageEnglish
Number of pages14
JournalIEEE - ACM Transactions on Computational Biology and Bioinformatics
Early online date9 Feb 2018
Publication statusE-pub ahead of print - 9 Feb 2018

Keywords

  • stochastic reaction networks, probabilistic model checking, organisation theory, coarse-graining, refinement