Fundamental boolean network modelling for genetic regulatory pathways : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

A Boolean model is a switch-like behaviour model of which it is easy to ignore any effects at the intermediate levels. Boolean modelling has been applied in many areas, including mammalian cell cycle networks. However, little effort has been put into the consideration of activation, inhibition and protein decay networks to designate the direct roles of a gene or a synthesised protein, as an activator or inhibitor of a target gene. Hence, we proposed to split the conventional Boolean functions at the subfunction level into activation and inhibition domains, taking into account the effectiveness of protein decay. As a consequence, two novel data-driven Boolean models for genetic regulatory pathways, namely the fundamental Boolean model (FBM) and the temporal fundamental Boolean model (TFBM), have been proposed to draw insights into gene activation, inhibition, and protein decay. The novel Boolean models could reveal significant trajectories in genes and provide a new direction on Boolean modelling research. The proposed novel Boolean models are fine-grained. A novel network inference methodology named Orchard cube technology has been proposed to infer the related networks, namely fundamental Boolean networks (FBNs) and temporal fundamental Boolean networks (TFBNs) based on FBM and TFBM respectively. As a primary result of this study, an R package, called FBNNet, has been developed based on the proposed methodology and has been used to demonstrate the FBNs and TFBNs for mammalian cell cycle pathways and acute childhood leukaemia pathways respectively. Our experimental results show that the proposed FBM and TFBM could be used to explicitly reconstruct the internal networks of mammalian cell cycles and acute childhood leukaemia. Especially during the study, we produced the fundamental Boolean networks on the childhood acute lymphoblastic leukaemia gene expression data, which were produced in clinical settings. The pathways may be useful for pharmaceutical agents to identify any side effects when applying GC induced apoptosis on children