Automatic Network Reconstruction using ASP

TL;DR

This paper presents a declarative ASP-based method for automatic biological network reconstruction, enabling transparent, flexible, and comprehensive model exploration with competitive performance.

Contribution

It introduces an ASP-based approach that overcomes limitations of heuristic methods, providing a mathematically grounded, transparent, and elaboration-tolerant solution for network inference.

Findings

ASP approach explores all possible models

Method matches performance of specialized systems

Provides transparent and constraint-friendly modeling

Abstract

Building biological models by inferring functional dependencies from experimental data is an im- portant issue in Molecular Biology. To relieve the biologist from this traditionally manual process, various approaches have been proposed to increase the degree of automation. However, available ap- proaches often yield a single model only, rely on specific assumptions, and/or use dedicated, heuris- tic algorithms that are intolerant to changing circumstances or requirements in the view of the rapid progress made in Biotechnology. Our aim is to provide a declarative solution to the problem by ap- peal to Answer Set Programming (ASP) overcoming these difficulties. We build upon an existing approach to Automatic Network Reconstruction proposed by part of the authors. This approach has firm mathematical foundations and is well suited for ASP due to its combinatorial flavor providing a characterization of all models explaining a set of experiments. The usage of ASP has several ben- efits over the existing heuristic algorithms. First, it is declarative and thus transparent for biological experts. Second, it is elaboration tolerant and thus allows for an easy exploration and incorporation of biological constraints. Third, it allows for exploring the entire space of possible models. Finally, our approach offers an excellent performance, matching existing, special-purpose systems.