Background:A novel data-driven Boolean model,namely,the fundamental Boolean model(FBM),has been proposed to draw genetic regulatory insights into gene activation,inhibition,and protein decay,published in 2018.This nov...Background:A novel data-driven Boolean model,namely,the fundamental Boolean model(FBM),has been proposed to draw genetic regulatory insights into gene activation,inhibition,and protein decay,published in 2018.This novel Boolean model facilitates the analysis of the activation and inhibition pathways.However,the novel model does not handle the situation well,where genetic regulation might require more time steps to complete.Methods: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.Results: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-time series.The data contain gene expression measurements for 13 GC-sensitive 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).Conclusion:We conclude that the proposed TFBM unlocks their predecessor’s limitation,Le.,FBM,that could help pharmaceutical agents identify any side effects on clinic-related data.New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways.展开更多
文摘Background:A novel data-driven Boolean model,namely,the fundamental Boolean model(FBM),has been proposed to draw genetic regulatory insights into gene activation,inhibition,and protein decay,published in 2018.This novel Boolean model facilitates the analysis of the activation and inhibition pathways.However,the novel model does not handle the situation well,where genetic regulation might require more time steps to complete.Methods: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.Results: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-time series.The data contain gene expression measurements for 13 GC-sensitive 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).Conclusion:We conclude that the proposed TFBM unlocks their predecessor’s limitation,Le.,FBM,that could help pharmaceutical agents identify any side effects on clinic-related data.New hypotheses could be identified by analysing the extracted fundamental Boolean networks and analysing their up-regulatory and down-regulatory pathways.
