DocumentCode :
3194506
Title :
Infer gene regulatory networks from time series data with formal methods
Author :
Ceccarelli, Marco ; Cerulo, L. ; Santone, Antonella
Author_Institution :
Dept. of Sci. & Technol., Univ. of Sannio, Benevento, Italy
fYear :
2013
fDate :
18-21 Dec. 2013
Firstpage :
115
Lastpage :
120
Abstract :
Reverse engineering of regulatory relationships from genomics data is emerging as crucial to dissect the complex underlying regulatory mechanism occurring in a cell. In this paper we propose a novel reverse engineering algorithm that makes use of formal methods, usually adopted in engineering to specify and verify concurrent software and hardware systems. With a formal specification of gene regulatory hypotheses we are able to prove mathematically whether a time course experiment belongs or not to the formal specification, determining in fact whether a gene regulation exists or not. The method is capable to detect both direction and sign (inhibition/activation) of regulations whereas most of literature methods which are limited to undirected and/or unsigned relationships. The method, empirically evaluated on experimental and synthetic datasets, reaches high levels of accuracy, outperforming literature methods in terms of precision and recall, despite the computational cost increases exponentially with the size of the network.
Keywords :
cellular biophysics; formal specification; genetics; genomics; reverse engineering; time series; cellular biophysics; concurrent hardware systems; concurrent software systems; formal methods; formal specification; genomic data; infer gene regulatory networks; outperforming literature methods; reverse engineering algorithm; synthetic datasets; time series data; Biological system modeling; Data models; Gene expression; Model checking; Noise; Time series analysis; Formal Methods; Gene Regulatory Network; Reverse Engineering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2013 IEEE International Conference on
Conference_Location :
Shanghai
Type :
conf
DOI :
10.1109/BIBM.2013.6732473
Filename :
6732473
Link To Document :
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