Reverse engineering of gene regulation models from multi-condition experiments

Abstract

Reverse-engineering of quantitative, dynamic gene-regulatory network (GRN) models from time-series gene expression data is becoming important as such data are increasingly generated for research and other purposes. A key problem in the reverse-engineering process is the under-determined nature of these data. Because of this, the reverse-engineered GRN models often lack robustness and perform poorly when used to simulate system responses to new conditions. In this study, we present a novel method capable of inferring robust GRN models from multi-condition GRN experiments. This study uses two important computational intelligence methods: artificial neural networks and particle swarm optimization.