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Algorithms 2017, 10(1), 8; doi:10.3390/a10010008

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

1
IRCCyN UMR CNRS 6597 (Institut de Recherche en Communications et Cybernétique de Nantes), École Centrale de Nantes, 1 rue de la Noë, 44321 Nantes, France
2
National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan
3
Department of Computer Science, Tokyo Institute of Technology, 2-12-1 Oookayama, Meguro-ku, Tokyo 152-8552, Japan
This paper is an extended version of our paper published in Ben Abdallah, E.; Ribeiro, T.; Magnin, M.; Roux, O.; Inoue, K. Inference of Delayed Biological Regulatory Networks from Time Series Data. In Proceeding of the International Conference on Computational Methods in Systems Biology. 21–23 September 2016; pp. 30–48.
*
Author to whom correspondence should be addressed.
Academic Editor: Takeyuki Tamura
Received: 31 October 2016 / Revised: 13 December 2016 / Accepted: 20 December 2016 / Published: 9 January 2017
(This article belongs to the Special Issue Biological Networks)
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Abstract

Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems’ dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method. View Full-Text
Keywords: learning models; dynamics modeling; delayed biological regulatory networks; timed automata networks; time series data learning models; dynamics modeling; delayed biological regulatory networks; timed automata networks; time series data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Ben Abdallah, E.; Ribeiro, T.; Magnin, M.; Roux, O.; Inoue, K. Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data. Algorithms 2017, 10, 8.

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