Learning Boolean logic models of signaling networks with ASP - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Theoretical Computer Science Year : 2015

Learning Boolean logic models of signaling networks with ASP

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 a separate appendix.
Fichier principal
Vignette du fichier
paper.pdf (1.5 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01058610 , version 1 (27-08-2014)

Identifiers

Cite

Santiago Videla, Carito Guziolowski, Federica Eduati, Sven Thiele, Martin Gebser, et al.. Learning Boolean logic models of signaling networks with ASP. Theoretical Computer Science, 2015, 599, pp.79-101. ⟨10.1016/j.tcs.2014.06.022⟩. ⟨hal-01058610⟩
843 View
742 Download

Altmetric

Share

Gmail Facebook X LinkedIn More