A standardized SBML/PRISM benchmark library for stochastic model checking in synthetic biology

Stochastic model checking is a powerful verification technique used in engineering to assess system reliability and correctness. Many synthetic biological systems, including chemical reaction networks, can be modeled as stochastic processes, making stochastic model checking well suited for evaluating and improving their performance. However, direct application in synthetic biology faces domain-specific challenges that often require adapting existing analysis techniques and developing new algorithms that scale to biological complexity. To support this software development, we present a curated library of case studies representing biologically inspired stochastic models with unbounded state spaces. Each case study is provided in both SBML and PRISM formats to support accessibility and interoperability. By openly releasing the library and encouraging community contributions, this work aims to improve reproducibility, enable meaningful tool comparisons, and accelerate development of robust software infrastructure for stochastic model checking in synthetic biology.