TY  - JOUR
A1  - Frioux, Clémence
A1  - Schaub, Torsten H.
A1  - Schellhorn, Sebastian
A1  - Siegel, Anne
A1  - Wanko, Philipp
T1  - Hybrid metabolic network completion
JF  - Theory and practice of logic programming
N2  - 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 dingo 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.
KW  - answer set programming
KW  - metabolic network
KW  - gap-filling
KW  - linear programming
KW  - hybrid solving
KW  - bioinformatics
Y1  - 2018
U6  - https://doi.org/10.1017/S1471068418000455
SN  - 1471-0684
SN  - 1475-3081
VL  - 19
IS  - 1
SP  - 83
EP  - 108
PB  - Cambridge University Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Gebser, Martin
A1  - Schaub, Torsten H.
A1  - Thiele, Sven
A1  - Veber, Philippe
T1  - Detecting inconsistencies in large biological networks with answer set programming
JF  - Theory and practice of logic programming
N2  - We introduce an approach to detecting inconsistencies in large biological networks by using answer set programming. 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 answer set programming 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.
KW  - answer set programming
KW  - bioinformatics
KW  - consistency
KW  - diagnosis
Y1  - 2011
U6  - https://doi.org/10.1017/S1471068410000554
SN  - 1471-0684
VL  - 11
IS  - 5-6
SP  - 323
EP  - 360
PB  - Cambridge Univ. Press
CY  - New York
ER  -