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Hybrid metabolic network completion

Published online by Cambridge University Press:  09 November 2018

CLÉMENCE FRIOUX Open the ORCID record for CLÉMENCE FRIOUX [Opens in a new window] ,
TORSTEN SCHAUB Open the ORCID record for TORSTEN SCHAUB [Opens in a new window] ,
SEBASTIAN SCHELLHORN ,
ANNE SIEGEL  and
PHILIPP WANKO
CLÉMENCE FRIOUX
Affiliation:
Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France (e-mail: [email protected])
TORSTEN SCHAUB
Affiliation:
Inria, Rennes, France Universität Potsdam, Potsdam, Germany (e-mail: [email protected])
SEBASTIAN SCHELLHORN
Affiliation:
Universität Potsdam, Germany (e-mail: [email protected])
ANNE SIEGEL
Affiliation:
Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France (e-mail: [email protected])
PHILIPP WANKO
Affiliation:
Universität Potsdam, Potsdam, Germany (e-mail: [email protected])
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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.

Keywords

answer set programmingmetabolic networkgap-fillinglinear programminghybrid solvingbioinformatics
Type
Technical Note
Information
Theory and Practice of Logic Programming , Volume 19 , Issue 1 , January 2019 , pp. 83 - 108
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

This work was partially funded by DFG grant SCHA 550/9 and 11 and benefited from the support of the French Government via the National Research Agency investment expenditure program IDEALG ANR-10-BTBR-04.

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