Hybrid metabolic network completion. Issue 1 (9th November 2018)

• Record Type: Journal Article
• Authors: FRIOUX, CLÉMENCE; SCHAUB, TORSTEN; SCHELLHORN, SEBASTIAN; SIEGEL, ANNE; WANKO, PHILIPP
• Is Part Of: Theory and practice of logic programming. Volume 19:Issue 1(2019)
• Page Start: 83
• Page End: 108
• Publication Date: 2018-11-09
• Subjects: answer set programming, -- metabolic network, -- gap-filling, -- linear programming, -- hybrid solving, -- bioinformatics; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068418000455
• Languages: English
• ISSNs: 1471-0684

Abstract

Abstract: Metabolic networks play a crucial role in biology since they capture all chemical reactions in an organism. While there are networks of high quality for many model organisms, networks for less studied organisms are often of poor quality and suffer from incompleteness. To this end, we introduced in previous work an answer set programming (ASP)-based approach to metabolic network completion. Although this qualitative approach allows for restoring moderately degraded networks, it fails to restore highly degraded ones. This is because it ignores quantitative constraints capturing reaction rates. To address this problem, we propose a hybrid approach to metabolic network completion that integrates our qualitative ASP approach with quantitative means for capturing reaction rates. We begin by formally reconciling existing stoichiometric and topological approaches to network completion in a unified formalism. With it, we develop a hybrid ASP encoding and rely upon the theory reasoning capacities of the ASP system clingo for solving the resulting logic program with linear constraints over reals. We empirically evaluate our approach by means of the metabolic network of Escherichia coli . Our analysis shows that our novel approach yields greatly superior results than obtainable from purely qualitative or quantitative approaches.