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 (reverseengineering) of dynamic mechanistic GRN models from gene-expression timecourse 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 singlestage 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.