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However, the novel model does not handle the situation well, where genetic regulation might require more time steps to complete.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Here, we propose extending the fundamental Boolean modelling to address the issue that some gene regulations might require more time steps to complete than others. We denoted this extension model as the temporal fundamental Boolean model (TFBM) and related networks as the temporal fundamental Boolean networks (TFBNs). The leukaemia microarray datasets downloaded from the National Centre for Biotechnology Information have been adopted to demonstrate the utility of the proposed TFBM and TFBNs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We developed the TFBNs that contain 285 components and 2775 Boolean rules based on TFBM on the leukaemia microarray datasets, which are in the form of short\u2010time series. The data contain gene expression measurements for 13 GC\u2010sensitive children under therapy for acute lymphoblastic leukaemia, and each sample has three time points: 0 hour (before GC treatment), 6\/8 hours (after GC treatment) and 24 hours (after GC treatment).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We conclude that the proposed TFBM unlocks their predecessor\u2019s limitation, <jats:italic>i.e.<\/jats:italic>, FBM, that could help pharmaceutical agents identify any side effects on clinic\u2010related data. 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