Infer Gene Regulatory Networks from Time Series Data with Probabilistic Model Checking

Giuseppe De Ruvo, Vittoria Nardone, Antonella Santone, Michele Ceccarelli, Luigi Cerulo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)

Abstract

Gene regulatory relationships constitute a complex mechanism of interactions adopted by cells to control behaviours and functions of a living organism. The identification of such relationships from genomics data through a computational approach is a challenging task as the large number of possible solutions is typically high in contrast to the number of available independent data points. Literature approaches address the problem by reducing the search space and/or extend the amount of independent information. In this paper we propose a probabilistic variant of a previous proposed approach based on formal methods. The method starts with a formal specification of gene regulatory hypotheses and then determines which is the probability that such hypotheses are explained by the available time series data. Both direction and sign (inhibition/activation) of regulations can be detected whereas most of literature methods are limited just to undirected and/or unsigned relationships. We empirically evaluated the probabilistic variant on experimental and synthetic datasets showing that the levels of accuracy are in most cases higher than those obtained with the previous method, outperforming, indeed, the current state of art.

Original languageEnglish
Title of host publicationProceedings - 3rd FME Workshop on Formal Methods in Software Engineering, Formalise 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages26-32
Number of pages7
ISBN (Print)9781479919345
DOIs
Publication statusPublished - 24 Jul 2015
Event3rd FME Workshop on Formal Methods in Software Engineering, Formalise 2015 - Florence, Italy
Duration: 18 May 2015 → …

Other

Other3rd FME Workshop on Formal Methods in Software Engineering, Formalise 2015
CountryItaly
CityFlorence
Period18/5/15 → …

    Fingerprint

Keywords

  • Formal methods
  • Gene regulatory networks
  • Reverse engineering

ASJC Scopus subject areas

  • Software

Cite this

Ruvo, G. D., Nardone, V., Santone, A., Ceccarelli, M., & Cerulo, L. (2015). Infer Gene Regulatory Networks from Time Series Data with Probabilistic Model Checking. In Proceedings - 3rd FME Workshop on Formal Methods in Software Engineering, Formalise 2015 (pp. 26-32). [7166694] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FormaliSE.2015.12