Automated model refinement using perturbation-observation pairs.

Abstract

In modeling signal transduction networks, it is common to manually integrate experimental evidence through a process that involves trial and error constrained by domain knowledge. We implement a genetic algorithm-based workflow (boolmore) to streamline Boolean model refinement. Boolmore adjusts the functions of the model to enhance agreement with a corpus of curated perturbation-observation pairs. It leverages existing mechanistic knowledge to automatically limit the search space to biologically plausible models. We demonstrate boolmore's effectiveness in a published plant signaling model that exemplifies the challenges of manual model construction and refinement. The refined models surpass the accuracy gain achieved over two years of manual revision and yield new, testable predictions. By automating the laborious task of model validation and refinement, this workflow is a step towards fast, fully automated, and reliable model construction.