Foundations of logic programming; (2nd extended ed.)
Foundations of logic programming; (2nd extended ed.)
Theoretical Computer Science
Theories for mutagenicity: a study in first-order and feature-based induction
Artificial Intelligence - Special volume on empirical methods
Revising regulatory networks: from expression data to linear causal models
Journal of Biomedical Informatics
Recovering metabolic pathways via optimization
Bioinformatics
An efficient solver for weighted Max-SAT
Journal of Global Optimization
Metabolic Network Expansion with Answer Set Programming
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
What is answer set programming?
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Evaluating abductive hypotheses using an EM algorithm on BDDs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Clasp: a conflict-driven answer set solver
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
GrinGo: a new grounder for answer set programming
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Analyzing pathways using SAT-based approaches
AB'07 Proceedings of the 2nd international conference on Algebraic biology
Automatic revision of metabolic networks through logical analysis of experimental data
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Inference of gene relations from microarray data by abduction
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
Logic programming for Boolean networks
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
A constraint solver for flexible protein models
Journal of Artificial Intelligence Research
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This paper contributes to a line of research which aims to combine numerical information with logical inference in order to find the most likely states of a biological system under various (actual or hypothetical) constraints. To this end, we build upon a state-of-the-art approach that employs weighted Boolean constraints to represent and reason about biochemical reaction networks. Our first contribution is to show how this existing method fails to deal satisfactorily with networks that contain cycles. Our second contribution is to define a new method which correctly handles such cases by exploiting the formalism of Answer Set Programming (ASP). We demonstrate the significance of our results on two case-studies previously studied in the literature.