TY - GEN
T1 - Semi-quantitative Abstraction and Analysis of Chemical Reaction Networks
AU - Češka, Milan
AU - Křetínský, Jan
N1 - Publisher Copyright:
© The Author(s). 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85069863997&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-25540-4_28
DO - 10.1007/978-3-030-25540-4_28
M3 - Conference contribution
AN - SCOPUS:85069863997
SN - 9783030255398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 475
EP - 496
BT - Computer Aided Verification - 31st International Conference, CAV 2019, Proceedings
A2 - Dillig, Isil
A2 - Tasiran, Serdar
PB - Springer Verlag
T2 - 31st International Conference on Computer Aided Verification, CAV 2019
Y2 - 15 July 2019 through 18 July 2019
ER -