Inference of Qualitative Models from Steady-State Data via Weighted MaxSMT

TL;DR

This paper presents a robust method using weighted MaxSMT to infer qualitative biological models from steady-state data, effectively handling measurement errors and conflicting observations.

Contribution

The authors introduce a weighted MaxSMT-based inference approach that manages uncertainty and conflicts in biological data for qualitative model inference.

Findings

Successfully inferred neural cell differentiation models from large gene networks.

Effectively handled measurement errors and conflicting constraints in biological data.

Supported Boolean and multi-valued variable domains with various types of observations.

Abstract

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.