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Bayesian Networks (BNs) are used in a wide range of applications, being the representation of regulatory networks a recurrent one. Nowadays great interest is dedicated to the problem of inferring the network's structure solely from the data. Aiming more precise results, the inclusion of extra knowledge in the inference process has been already suggested, as well as a Bayesian coupling scheme for learning genetic regulatory networks from a combination of related data sets which were obtained under different experimental conditions and are therefore potentially associated with different active sub-pathways. Furthermore, this approach has been combined to a MCMC sampling scheme and it has been verified that due to the complexity of the model, the MCMC suffered from poor convergence. We now propose the use of a Metropolis Coupled Markov Chain Monte Carlo (MC)3 algorithm in order to improve the mixing and convergence of the inference process.