Semi-quantitative Abstraction and Analysis of Chemical Reaction Networks (Extended Abstract)

Semi-quantitative Abstraction and Analysis of Chemical Reaction Networks (Extended Abstract)

Paper in proceedings (conference paper)

Analysis of large continuous-time stochastic systems is a computationally intensive task. In this work we focus on population models arising from chemical reaction networks (CRNs), which play a fundamental role in analysis and design of biochemical systems. Many relevant CRNs are particularly challenging for existing techniques due to complex dynamics including stochasticity, stiffness or multimodal population distributions. We propose a novel approach allowing not only to predict, but also to explain both the transient and steady-state behaviour. It focuses on qualitative description of the behaviour and aims at quantitative precision only in orders of magnitude. First we build a compact understandable model, which we then crudely analyse. As demonstrated on complex CRNs from literature, our approach reproduces the known results, but in contrast to the state-of-the-art methods, it runs with virtually no computational cost and thus offers unprecedented scalability.

Analysis of large continuous-time stochastic systems is a computationally intensive task. In this work we focus on population models arising from chemical reaction networks (CRNs), which play a fundamental role in analysis and design of biochemical systems. Many relevant CRNs are particularly challenging for existing techniques due to complex dynamics including stochasticity, stiffness or multimodal population distributions. We propose a novel approach allowing not only to predict, but also to explain both the transient and steady-state behaviour. It focuses on qualitative description of the behaviour and aims at quantitative precision only in orders of magnitude. First we build a compact understandable model, which we then crudely analyse. As demonstrated on complex CRNs from literature, our approach reproduces the known results, but in contrast to the state-of-the-art methods, it runs with virtually no computational cost and thus offers unprecedented scalability.

chemical reaction networks, continuous-time Markov chains, population level abstraction, semiquantitative reasoning 

chemical reaction networks, continuous-time Markov chains, population level abstraction, semiquantitative reasoning 

ČEŠKA, M.; KŘETÍNSKÝ, J.

2020

17.09.2019

Springer International Publishing

Trieste

978-3-030-31303-6

Proceedings of the 17th International Conference on Computational Methods in Systems Biology

Lecture Notes in Bioinformatics

337

341

5

https://www.fit.vut.cz/research/publication/12151/

2019_Chapter_Semi-quantitativeAbstractionAn