Inference of Gene Predictor Set Using Boolean Satisfiability

TL;DR

This paper presents a SAT-based computational method to infer gene predictor sets from steady state gene expression data, aiding the modeling of gene regulatory networks in genomics.

Contribution

The paper introduces a novel SAT-based algorithm that infers gene predictors from attractor states, integrating biological constraints into a formal satisfiability framework.

Findings

Successfully applied to melanoma data

Identified likely gene predictor sets

Demonstrated effectiveness of SAT approach

Abstract

The inference of gene predictors in the gene regulatory network has become an important research area in the genomics and medical disciplines. Accurate predicators are necessary for constructing the GRN model and to enable targeted biological experiments that attempt to confirm or control the regulation process. In this paper, we implement a SAT-based algorithm to determine the gene predictor set from steady state gene expression data (attractor states). Using the attractor states as input, the states are ordered into attractor cycles. For each attractor cycle ordering, all possible predictors are enumerated and a CNF expression is formulated which encodes these predictors and their biological constraints. Each CNF is explored using a SAT solver to find candidate predictor sets. Statistical analysis of the results selects the most likely predictor set of the GRN corresponding to the attractor data. We demonstrate our algorithm on attractor state data from a melanoma study, and present our predictor set results.