| Abstract |
This chapter introduces to a study aiming at comprehensively understanding the transcription factor regulatory networks (TFRNs) that govern the process of a cell differentiation. Here we focus on the adipocyte differentiation. For the cell differentiation, we inferred its TFRN using the Bayesian network (BN) method. BNs have been widely used to estimate TFRNs. Many BN methods have been developed to estimate networks from TF expression data. However, BN-based methods require huge computational time to estimate large-scale networks. This chapter introduces to a BN-based deterministic method with reduced computational time. This approach generates all the combinational subnetworks of three TFs, estimates networks of the subnetworks by BN, and unites the networks into a single large network. This method decreases the search space of predicting TFRNs without degrading the solution accuracy compared with the greedy hill climbing (GHC) method. This chapter also presents a massively parallel implementation for the BN-based inference of TFRNs. The system enables us to estimate large-scale (>10,000 transcripts) multiple TFRNs from expression profiles of various tissues and conditions. The comparison among estimated TFRNs of adipose tissues with stimulus induction is conducted. The various regulations to Ucp1 (uncoupled protein 1) in those networks may reflect different responses of the tissues under the stimulus induction.
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