A Two-Stage Inference Algorithm for Gene Regulation Network Models.

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.