Fundamental Boolean network modelling for childhood acute lymphoblastic leukaemia pathways

Abstract

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, i.e., 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.

Author summary

Boolean modelling has been applied in numerous areas. However, minimal effort has been put into creating activation, inhibition and protein decay networks. In the previous study, we proposed a novel concept, namely, fundamental Boolean model, to address these issues. However, it does not reflect that genetic regulation might require more time steps to complete. Hence, in this paper, we proposed extending the original model to temporal fundamental Boolean model and demonstrated the new model with the leukaemia datasets downloaded from NCBI. Our result shows that the proposed model’s capacity could identify crucial signalling pathways for glucocorticoid treated childhood leukaemia patients.

Graphical abstract

Keywords

Boolean modelling / Boolean network / time series data / network inference / data-driven boolean modelling / fundamental boolean model / fundamental boolean networks / orchard cube

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SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/J-QB-021-0280.

ACKNOWLEDGEMENTS

We acknowledge the Centre for Advanced Computational Solutions (C-fACS) at Lincoln University, New Zealand for facilitating this research.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Leshi Chen, Don Kulasiri and Sandhya Samarasinghe declare that they have no conflict of interests or competing financial interests. All procedures performed in studies were in accordance with the ethical standards of the institution or practice at which the studies were conducted, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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