Investigating issues in the reconstructability of genetic regulatory networks

Reverse engineering genetic regulatory networks (GRNs) is greatly undetermined by the data available. We need to understand the plausibility of a recovered GRN, but little is known about the correlation between matching the target expression vector and recovery of the target GRN. Here, we explore this and related issues and claim that (i) evolved target GRNs are more reliably reconstructed by evolutionary algorithms (EAs) than are 'random' target GRNs, and (ii) there is often no correlation between the best fit expression vector and recovery of the target GRN. Put together, this suggests that EA methods for biological-GRN reverse-engineering are favoured, even if other methods more closely match the target expression vector(s).