Int. J. of Computational Biology and Drug Design   »   2016 Vol.9, No.1/2

 

 

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

 

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

 

Available online: 26 Feb 2016

 

 

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