Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

Algorithms

Tony Ribeiro¹  Olivier Roux¹  Emna Ben Abdallah¹  Morgan Magnin¹  Katsumi Inoue² 

[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;National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan;

关键词: learning models;    dynamics modeling;    delayed biological regulatory networks;    timed automata networks;    time series data;   

DOI  :  10.3390/a10010008

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown