Article (Scientific journals)
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET
Rodriguez, Ana; Crespo, Isaac; Androsova, Ganna et al.
2015In PLoS ONE, 10 (6), p. 0127216
Peer Reviewed verified by ORBi


Full Text
Publisher postprint (2.81 MB)

All documents in ORBilu are protected by a user license.

Send to


Abstract :
[en] High-throughput technologies have led to the generation of an increasing amount of data in different areas of biology. Datasets capturing the cell’s response to its intra- and extra-cellular microenvironment allows such data to be incorporated as signed and directed graphs or influence networks. These prior knowledge networks (PKNs) represent our current knowledge of the causality of cellular signal transduction. New signalling data is often examined and interpreted in conjunction with PKNs. However, different biological contexts, such as cell type or disease states, may have distinct variants of signalling pathways, resulting in the misinterpretation of new data. The identification of inconsistencies between measured data and signalling topologies, as well as the training of PKNs using context specific datasets (PKN contextualization), are necessary conditions to construct reliable, predictive models, which are current challenges in the systems biology of cell signalling. Here we present PRUNET, a user-friendly software tool designed to address the contextualization of a PKNs to specific experimental conditions. As the input, the algorithm takes a PKN and the expression profile of two given stable steady states or cellular phenotypes. The PKN is iteratively pruned using an evolutionary algorithm to perform an optimization process. This optimization rests in a match between predicted attractors in a discrete logic model (Boolean) and a Booleanized representation of the phenotypes, within a population of alternative subnetworks that evolves iteratively. We validated the algorithm applying PRUNET to four biological examples and using the resulting contextualized networks to predict missing expression values and to simulate well-characterized perturbations. PRUNET constitutes a tool for the automatic curation of a PKN to make it suitable for describing biological processes under particular experimental conditions. The general applicability of the implemented algorithm makes PRUNET suitable for a variety of biological processes, for instance cellular reprogramming or transitions between healthy and disease states.
Disciplines :
Computer science
Author, co-author :
Rodriguez, Ana 
Crespo, Isaac  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Androsova, Ganna ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
del Sol Mesa, Antonio ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
 These authors have contributed equally to this work.
External co-authors :
Language :
Title :
Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET
Publication date :
09 June 2015
Journal title :
Publisher :
Public Library of Science, San Franscisco, United States - California
Volume :
Issue :
Pages :
Peer reviewed :
Peer Reviewed verified by ORBi
Funders :
The authors received no specific funding for this work.
Available on ORBilu :
since 25 July 2015


Number of views
98 (10 by Unilu)
Number of downloads
90 (4 by Unilu)

Scopus citations®
Scopus citations®
without self-citations
WoS citations


Similar publications

Contact ORBilu