Detecting Inconsistencies in Large Biological Networks with Answer Set Programming

TL;DR

This paper presents a novel ASP-based method for detecting and explaining inconsistencies in large biological networks, aiding in identifying unreliable data and missing reactions.

Contribution

It introduces an ASP approach to check consistency in large biological data sets and extends it to provide minimal conflict explanations.

Findings

Effective detection of inconsistencies in large biological networks

Ability to identify unreliable data or missing reactions

Scalable approach for large-scale biological data analysis

Abstract

We introduce an approach to detecting inconsistencies in large biological networks by using Answer Set Programming (ASP). To this end, we build upon a recently proposed notion of consistency between biochemical/genetic reactions and high-throughput profiles of cell activity. We then present an approach based on ASP to check the consistency of large-scale data sets. Moreover, we extend this methodology to provide explanations for inconsistencies by determining minimal representations of conflicts. In practice, this can be used to identify unreliable data or to indicate missing reactions.