Organisation-Oriented Coarse Graining and Refinement of Stochastic Reaction Networks

Chunyan Mu, Peter Dittrich, David Parker, Jonathan E. Rowe

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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.
Original languageEnglish
Article number8288662
Pages (from-to)1152-1166
Number of pages15
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume15
Issue number4
Early online date9 Feb 2018
DOIs
Publication statusPublished - 31 Jul 2018

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Reaction Network
Stochastic Networks
Markov Chains
Coarse-graining
Refinement
Markov processes
Chemical Reaction Networks
Markov Chain Model
Continuous-time Markov Chain
Prediction
Formal Analysis
Stochastic models
Master Equation
Discrete Model
Long-run
Quantitative Analysis
Experiment
Stochastic Model
Chemical reactions
Simplify

Cite this

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Organisation-Oriented Coarse Graining and Refinement of Stochastic Reaction Networks. / Mu, Chunyan; Dittrich, Peter; Parker, David; Rowe, Jonathan E.

In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 15, No. 4, 8288662, 31.07.2018, p. 1152-1166.

Research output: Contribution to journalArticleResearchpeer-review

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