用加权最大SMT从稳态数据推断生物定性模型，抗测量误差且能处理冲突约束。
Inference of Qualitative Models from Steady-State Data via Weighted MaxSMT
- 将不确定的生物观测转为加权软约束，让求解器选出最符合数据的模型。
- 在200至1300个基因的网络中，成功推断出神经细胞分化模型。
- 适合需要从噪声数据中构建可靠定性模型的研究者使用。
定性模型是建模复杂生物系统的重要工具。尽管自动化推理与符号编码的进步使模型推断更加严谨，但该过程仍极易受干扰。首先，生物测量误差会传播至形式化模型中；其次，当模型规格不满足时，难以区分根本设计缺陷与微小技术错误，导致模型常被低估，无法确定哪些观察仍可安全纳入。为此，我们提出一种基于加权最大SMT的鲁棒推断方法。通过将不确定的生物观测编码为加权软约束，该方法可在存在部分冲突约束的情况下，仍能识别出最符合观测数据的模型。本方法支持布尔值和多值变量域，兼容由离散化生成的水平约束（level constraints）与差分表达生成的排序约束（ordering constraints）。我们证明，该方法可成功从包含200至1300个基因的先验知识网络中，利用所有基因的排序约束推断出神经细胞分化模型。
原文摘要 · Abstract (English)
Qualitative models provide crucial instruments for modelling complex biological systems. While advances in automated reasoning and symbolic encodings have enabled rigorous inference of these models from data, the process remains highly fragile. First, biological measurement errors inevitably propagate into formal model specifications. Second, when a specification becomes unsatisfiable, distinguishing between fundamental design flaws and minor technical errors is notoriously difficult. This uncertainty often leads to under-specification, as it is unclear which observations are still ``safe'' to incorporate. To overcome these challenges, we introduce a robust inference method based on weighted MaxSMT. By encoding uncertain biological observations as weighted soft constraints, our approach enables the solver to identify a model best reflecting the observations, even with some conflicting constraints. Our method allows for Boolean and multi-valued variable domains, alongside observations derived from discretisation (level constraints) and differential expression (ordering constraints). We show our approach can be used to successfully infer neural cell differentiation models from prior-knowledge networks with 200--1300 genes using ordering constraints on all included genes.
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