@article{VidelaGuziolowskiEduatietal.2015,
  author    = {Videla, Santiago and Guziolowski, Carito and Eduati, Federica and Thiele, Sven and Gebser, Martin and Nicolas, Jacques and Saez-Rodriguez, Julio and Schaub, Torsten H. and Siegel, Anne},
  title     = {Learning Boolean logic models of signaling networks with ASP},
  series = {Theoretical computer science},
  volume    = {599},
  journal   = {Theoretical computer science},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3975},
  doi       = {10.1016/j.tcs.2014.06.022},
  pages     = {79 -- 101},
  year      = {2015},
  abstract  = {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.},
  language  = {en}
}
@article{FloeterNicolasSchaubetal.2004,
  author    = {Fl{\"o}ter, Andr{\´e} and Nicolas, Jacques and Schaub, Torsten H. and Selbig, Joachim},
  title     = {Threshold extraction in metabolite concentration data},
  year      = {2004},
  abstract  = {Motivation: Continued development of analytical techniques based on gas chromatography and mass spectrometry now facilitates the generation of larger sets of metabolite concentration data. An important step towards the understanding of metabolite dynamics is the recognition of stable states where metabolite concentrations exhibit a simple behaviour. Such states can be characterized through the identification of significant thresholds in the concentrations. But general techniques for finding discretization thresholds in continuous data prove to be practically insufficient for detecting states due to the weak conditional dependences in concentration data. Results: We introduce a method of recognizing states in the framework of decision tree induction. It is based upon a global analysis of decision forests where stability and quality are evaluated. It leads to the detection of thresholds that are both comprehensible and robust. Applied to metabolite concentration data, this method has led to the discovery of hidden states in the corresponding variables. Some of these reflect known properties of the biological experiments, and others point to putative new states},
  language  = {en}
}
@article{FloeterNicolasSchaubetal.2003,
  author    = {Fl{\"o}ter, Andr{\´e} and Nicolas, Jacques and Schaub, Torsten H. and Selbig, Joachim},
  title     = {Threshold extraction in metabolite concentration data},
  year      = {2003},
  language  = {en}
}