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Developing computational models paves the way to understanding, predicting, and influencing the long-term behavior of genomic regulatory systems. However, several major challenges have to be addressed before such models are successfully applied in practice. Their inherent high complexity requires strategies for complexity reduction. Reducing the complexity of the model by removing genes and interpreting them as latent variables leads to the problem of selecting which states and theirdoi:10.1109/tsp.2010.2050314 fatcat:zaphbtphkjhn3beoebkwrlfv7y