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A systematic approach using a combination of entropy, pair-wise dependency and Bayesian approach is used to gain insight into the dependencies and underlying network structure. Pairwise dependencies are estimated using linear correlation and mutual information. An algorithm is proposed to identify statistically significant mutual information estimates. The robustness of the dependencies and the network structure to decreasing number of colonies (colony size) and perturbation is investigated. Perturbation is achieved by generating bootstrap samples. 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