Retrieving common dynamics of gene regulatory networks under various perturbations

Recently, with the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to extract meaningful structure from inherently heterogeneous data. Little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. Challenges due to the lack of knowledge may cause bias or uncertainty in identifying parameters or inferring the GRN structure. We propose a new algorithm which enables us to estimate bias error due to the effect of perturbations, and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations inspired by “image repairing” in computer vision [1].