Title: A two-stage inference algorithm for gene regulation network models

Authors: Alexandru Mizeranschi; Huiru Zheng; Paul Thompson; Werner Dubitzky

Addresses: Biomedical Sciences Research Institute, University of Ulster, Coleraine BT52 1SA, UK ' Computer Science Research Institute, University of Ulster, Jordanstown BT37 0QB, UK ' Biomedical Sciences Research Institute, University of Ulster, Coleraine BT52 1SA, UK ' Biomedical Sciences Research Institute, University of Ulster, Coleraine BT52 1SA, UK

Abstract: Modelling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern systems biology investigations. An important and unsolved problem in this area is the automated inference (reverse-engineering) of dynamic mechanistic GRN models from gene-expression time-course data. The conventional single-stage algorithm determines the values of all model parameters simultaneously, whereas recent two-stage algorithms can potentially improve the performance (accuracy) of single-stage approaches. The objective of this study is to compare the performance of the conventional single-stage and a novel version of the modern two-stage algorithm. We based this study on our implementation of a multi-swarm particle swarm optimisation process. A particular focus of this study is placed on the comparison of the computational performance of the single-stage vs. two-stage algorithm. Our results suggest that the 2-stage approach outperforms the single-stage methods by far in terms of model inference speed without loss of accuracy.

Keywords: system biology; gene regulation; gene regulatory networks; GRNs; model inference; reverse engineering; two-stage algorithms; modelling; simulation; particle swarm optimisation; PSO.

DOI: 10.1504/IJCBDD.2016.074981

International Journal of Computational Biology and Drug Design, 2016 Vol.9 No.1/2, pp.6 - 24

Available online: 26 Feb 2016

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