Abstract
This paper presents a method for regulatory network reconstruction from experimental data. We propose a mathematical model for regulatory interactions, based on the work of Thomas et al. [25] extended with a stochastic element and provide an algorithm for reconstruction of such models from gene expression time series. We examine mathematical properties of the model and the reconstruction algorithm and test it on expression profiles obtained from numerical simulation of known regulatory networks. We compare the reconstructed networks with the ones reconstructed from the same data using Dynamic Bayesian Networks and show that in these cases our method provides the same or better results. The supplemental materials to this article are available from the website http://bioputer.mimuw.edu.pl/papers/cmsb06
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Wilczyński, B., Tiuryn, J. (2006). Regulatory Network Reconstruction Using Stochastic Logical Networks. In: Priami, C. (eds) Computational Methods in Systems Biology. CMSB 2006. Lecture Notes in Computer Science(), vol 4210. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11885191_10
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DOI: https://doi.org/10.1007/11885191_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46166-1
Online ISBN: 978-3-540-46167-8
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