We present a framework called the Reasoning Engine, which implements Satisfiability Modulo Theories (SMT)-based methods within a unified computational environment to address diverse biological analysis problems. The Reasoning Engine was used to reproduce results from key scientific studies, as well as supporting new research in stem cell biology. The framework utilizes an intermediate language for encoding partially specified discrete dynamical systems, which bridges the gap between high-level domain-specific languages and low-level SMT solvers. We provide this framework as open source together with various biological case studies, illustrating the synthesis, enumeration, optimization, and reasoning over models consistent with experimental observations to reveal novel biological insights.

中文翻译：

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推理引擎：基于可满足性模理论的框架，用于推理离散生物模型。

我们提出了一个称为推理引擎的框架，它在统一的计算环境中实现基于可满足性模理论（SMT）的方法来解决不同的生物分析问题。推理引擎用于重现关键科学研究的结果，并支持干细胞生物学的新研究。该框架利用中间语言对部分指定的离散动力系统进行编码，从而弥合了高级特定领域语言和低级 SMT 求解器之间的差距。我们以开源方式提供该框架以及各种生物学案例研究，说明了与实验观察一致的模型的合成、枚举、优化和推理，以揭示新的生物学见解。