  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.
Firstly, we abstract the CRN into a compact model preserving rough timing
information, distinguishing only signifcinatly different populations, but
capturing relevant sequences of behaviour. Secondly, we approximately analyse
the most probable temporal behaviours of the model through most probable
transitions. 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.
