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In synthetic biological systems, rare events can cause undesirable behavior leading to pathological effects. Due to their low observability, rare events are challenging to analyze using existing stochastic simulation methods. Chemical Reaction Networks (CRNs) are a general-purpose formal language for modeling chemical kinetics. This paper presents a fully automated approach to efficiently construct a large number of concurrent traces by expanding a sample of known traces. These traces constitute a partial state space containing only traces leading to a rare event of interest. This state space is then used to compute a lower bound for the rare event’s probability. We propose a novel approach for the analysis of highly concurrent CRNs, including a CRN reaction independence analysis and an algorithm that exploits CRN concurrency to rapidly enumerate parallel traces. We then present a novel algorithm to add cycles to a partial state space to further increase the rare event’s probability lower bound to its actual value. The resulting prototype tool, RAGTIMER, demonstrates improvement over stochastic simulation and probabilistic model checking. 

The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool support is available for computing basic properties such as expected rewards on basic models such as Markov chains. Previous editions of QComp, the comparison of tools for the analysis of quantitative formal models, focused on this setting. Many application scenarios, however, require more advanced property types such as LTL and parameter synthesis queries as well as advanced models like stochastic games and partially observable MDPs. For these, tool support is in its infancy today. This paper presents the outcomes of QComp 2023: a survey of the state of the art in quantitative verification tool support for advanced property types and models. With tools ranging from first research prototypes to well-supported integrations into established toolsets, this report highlights today’s active areas and tomorrow’s challenges in tool-focused research for quantitative verification. 

Rare events are known to potentially cause pathological behavior in biochemical reaction systems. It is important to understand the cause. However, rare events are challenging to analyze due to their extremely low observability. This paper presents a fully automated approach that rapidly generates a large number of execution traces guaranteed to reach user-specified rare-event states for Chemical Reaction Network (CRN) models. It is enabled by a unique combination of a multi-layered and service-oriented CRN formal modeling approach, a dependency graph method to aid the shortest rare-event trace generation, and randomized compositional testing. The resulting prototype tool shows marked improvement over stochastic simulation and probabilistic model checking and it offers insights into a CRN. 

Improving the scalability of probabilistic model checking (PMC) tools is crucial to the verification of real-world system designs. The STAMINA infinite-state PMC tool achieves scalability by iteratively constructing a partial state space for an unbounded continuous-time Markov chain model, where a majority of the probability mass resides. It then performs time-bounded transient PMC. It can efficiently produce an accurate probability bound to the property under verification. We present a new software architecture design and the C++ implementation of the STAMINA 2.0 algorithm, integrated with the STORM model checker. This open-source STAMINA implementation offers a high degree of modularity and provides significant optimizations to the STAMINA 2.0 algorithm. Performance improvements are demonstrated on multiple challenging benchmark examples, including hazard analysis of infinite-state combinational genetic circuits, over the previous STAMINA implementation. Additionally, its design allows for future customizations and optimizations to the STAMINA algorithm.