Stability of inferring gene regulatory structure with dynamic Bayesian networks

Though a plethora of techniques have been used to build gene regulatory networks (GRN) from time-series gene expression data, stabilities of such techniques have not been studied. This paper investigates the stability of GRN built using dynamic Bayesian networks (DBN) by synthetically generating gene expression time-series. Assuming scale-free topologies, sample datasets are drawn from DBN to evaluate the stability of estimating the structure of GRN. Our experiments indicate although high accuracy can be achieved with equal number of time points to the number of genes in the network, the presence of large numbers of false positives and false negatives deteriorate the stability of building GRN. The stability could be improved by gathering gene expression at more time points. Interestingly, large networks required less number of time points (normalized to the size of the network) than small networks to achieve the same level stability.