TY  - JOUR
A1  - Videla, Santiago
A1  - Guziolowski, Carito
A1  - Eduati, Federica
A1  - Thiele, Sven
A1  - Gebser, Martin
A1  - Nicolas, Jacques
A1  - Saez-Rodriguez, Julio
A1  - Schaub, Torsten H.
A1  - Siegel, Anne
T1  - Learning Boolean logic models of signaling networks with ASP
JF  - Theoretical computer science
N2  - Boolean networks provide a simple yet powerful qualitative modeling approach in systems biology. However, manual identification of logic rules underlying the system being studied is in most cases out of reach. Therefore, automated inference of Boolean logical networks from experimental data is a fundamental question in this field. This paper addresses the problem consisting of learning from a prior knowledge network describing causal interactions and phosphorylation activities at a pseudo-steady state, Boolean logic models of immediate-early response in signaling transduction networks. The underlying optimization problem has been so far addressed through mathematical programming approaches and the use of dedicated genetic algorithms. In a recent work we have shown severe limitations of stochastic approaches in this domain and proposed to use Answer Set Programming (ASP), considering a simpler problem setting. Herein, we extend our previous work in order to consider more realistic biological conditions including numerical datasets, the presence of feedback-loops in the prior knowledge network and the necessity of multi-objective optimization. In order to cope with such extensions, we propose several discretization schemes and elaborate upon our previous ASP encoding. Towards real-world biological data, we evaluate the performance of our approach over in silico numerical datasets based on a real and large-scale prior knowledge network. The correctness of our encoding and discretization schemes are dealt with in Appendices A-B. (C) 2014 Elsevier B.V. All rights reserved.
KW  - Answer set programming
KW  - Signaling transduction networks
KW  - Boolean logic models
KW  - Combinatorial multi-objective optimization
KW  - Systems biology
Y1  - 2015
U6  - https://doi.org/10.1016/j.tcs.2014.06.022
SN  - 0304-3975
SN  - 1879-2294
VL  - 599
SP  - 79
EP  - 101
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Fichte, Johannes Klaus
A1  - Szeider, Stefan
T1  - Backdoors to tractable answer set programming
JF  - Artificial intelligence
N2  - Answer Set Programming (ASP) is an increasingly popular framework for declarative programming that admits the description of problems by means of rules and constraints that form a disjunctive logic program. In particular, many Al problems such as reasoning in a nonmonotonic setting can be directly formulated in ASP. Although the main problems of ASP are of high computational complexity, complete for the second level of the Polynomial Hierarchy, several restrictions of ASP have been identified in the literature, under which ASP problems become tractable.
 In this paper we use the concept of backdoors to identify new restrictions that make ASP problems tractable. Small backdoors are sets of atoms that represent "clever reasoning shortcuts" through the search space and represent a hidden structure in the problem input. The concept of backdoors is widely used in theoretical investigations in the areas of propositional satisfiability and constraint satisfaction. We show that it can be fruitfully adapted to ASP. We demonstrate how backdoors can serve as a unifying framework that accommodates several tractable restrictions of ASP known from the literature. Furthermore, we show how backdoors allow us to deploy recent algorithmic results from parameterized complexity theory to the domain of answer set programming. (C) 2015 Elsevier B.V. All rights reserved.
KW  - Answer set programming
KW  - Backdoors
KW  - Computational complexity
KW  - Parameterized complexity
KW  - Kernelization
Y1  - 2015
U6  - https://doi.org/10.1016/j.artint.2014.12.001
SN  - 0004-3702
SN  - 1872-7921
VL  - 220
SP  - 64
EP  - 103
PB  - Elsevier
CY  - Amsterdam
ER  -