Dependency and in silico knockout analysis of the GEB052 model

As detailed in Figure ‎2.2.3 (Page 100, Section ‎2.2.4), within CNA it is possible to undertake an in-depth analysis of the effects of every model constituent on every model constituent. This is performed through the generation of a dependency matrix, which takes into account all possible signalling and feedback loops within the model to

157 determine the overall effect of every node on every node. Figure ‎4.3.1 shows the dependency matrix for the GEB052 model:

158

Figure ‎4.3.1: Dependency Matrix for GEB052 Model.

159 Dependency analysis of the full model, visualised above in Figure ‎4.3.1, shows that the overwhelming majority of dependencies within the model are ambivalent (meaning the source node exerts both a positive and negative influence on the target node). This correlates with the large number of feedback loops present within the model, as the feedback loops can lead to negative autoregulation and thus a negative as well as a positive influence. The high number of ambivalent dependencies further demonstrates the interconnectivity of the model, and provides an excellent starting point for in silico

knockout analysis as ambivalent dependencies are the ones most likely to change following network perturbation (Tian et al., 2013). In total, 2704 (52*52) dependencies were identified in the full GEB052 model. Of these, 896 were of no effect, 1710 were ambivalent, 33 were weak inhibitors, 63 were weak activators, 2 were strong activators and there were no strong inhibitors. The strong activators in the wild-type model were CD2 as a strong activator of cell death and CD40LG as a strong activator of inflammation.

As described in the Introduction, one of the benefits of computational biology is the ability to conduct numerous different analyses to unveil how models may change following a loss of network elements. It has been demonstrated across several studies that in silico knockout analysis can provide good insight into in vivo behaviour, for example by mimicking mutation effects through node deletion or activating hormone- dependent GR activation through turning node ON or OFF, provided the model is well- constructed.

To analyse the potential effects of the loss of network elements, in silico knockouts were performed against the highly connected (>10 interactions) nodes identified previously in Section ‎4.2.4 (Page 151), with the exception of model outputs (as they have no outgoing reactions, their removal would have no effect on other nodes within the model). The following summarises the effects of the knockouts on network relationships:

160 Scenario

Number of Each Dependency

No Effect Ambivalent Weak Inhibitor Weak Activator Strong Inhibitor Strong Activator Total Full Model 896 1710 33 63 0 2 2704 AP-1 KO 877 1581 66 75 0 2 2601 CREB1 KO 877 1626 33 63 0 2 2601 CREBBP/ EP300 KO 877 1576 61 85 0 2 2601 GR KO 1602 955 5 35 1 3 2601 HDAC1 KO 953 1541 36 65 0 6 2601 HSP90 KO 993 1481 53 68 0 6 2601 IL6 KO 877 1607 43 72 0 2 2601 IL10 KO 877 1626 33 63 0 2 2601 NFKB KO 877 1626 33 63 0 2 2601 SMAD3 KO 877 1626 33 63 0 2 2601 STAT3 KO 877 1574 63 85 0 2 2601 SUMO KO 917 1589 33 60 0 2 2601 TP53 KO 917 1579 36 67 0 2 2601

All KO scenarios above have only 2601 (51*51) total reactions (as opposed to 2704) due to the removal of the node (in turn removing all of its dependencies). As expected based on the number of interactions it participates in and its centrality to the network, the removal of the GR had the largest effect on the dependencies within the network:

161

Figure ‎4.3.2: Distribution of dependency alterations following GR KO.

The majority of dependency changes were from ambivalent factors to no effect, which is logically consistent when it is considered that many nodes will signal through the GR to affect others. Thus, removal of this central node removes many of the effects between other node pairs. However, there were also numerous changes from ambivalent factors to weak activators or weak inhibitors, as well as strong activators and strong inhibitors. Previous research (Hussain et al., 2015; Tian et al., 2013) has focussed primarily on the change to strong activators or strong inhibitors (as defined in Section ‎2.2.4, Page 97), as these are the changes most likely to show effects at the biological level. In addition to this, only strong activators and inhibitors are considered due to the sheer number of predictions generated. Across all knockout scenarios for the GEB052 model, 1249 predictions as to how model relationships change following a knockout was obtained. Even if changes from ambivalent factors to no effect are discarded (as there is no net change in activation or inhibition) then across all the knockout scenarios GEB052 produced 323 predictions in dependency changes. Analysis of this high number would be cumbersome, so there is a necessary focus on strong activators and strong inhibitors.

Following removal of the GR, one ambivalent dependency was changed to a strong activator, and another ambivalent dependency was changed to a strong inhibitor. Both

162 of these dependencies related to the output of cell death; in the full model, STAT5B is ambivalent to cell death, whereas following removal of the GR it becomes a strong inhibitor of cell death. Conversely, DAP3 is ambivalent to cell death in the full model, whereas following removal of the GR it becomes a strong activator of cell death. Identifying aberrant signalling following loss of functional GR is a key factor in improving therapies. It is known that STAT5 has an anti-apoptotic role in haematopoietic cells (Debierre-Grockiego, 2004), however it is interesting that the model indicates its pro-survival effect is stronger in glucocorticoid-resistant (which GR KO simulates) cells, which may point towards the potential of combining glucocorticoid treatment with anti-STAT5B treatments.

Other than the GR KO scenario, only two of the knockout scenarios detailed in Table ‎4.3.1 (Page 159) demonstrated changes to strong activators or inhibitors: HDAC1 KO and HSP90 KO. Their changes are tracked in Figure ‎4.3.3:

Figure ‎4.3.3: Distribution of dependency alterations following HDAC1 KO.

Loss of HDAC1 promoted four new strong activation dependencies. Dependency analysis takes into account all of the signalling within the model and thus even the effect of nodes on themselves can be seen. In the full model, DAXX was ambivalent to

163 itself and to SUMO, whilst SUMO was also ambivalent to itself. Loss of HDAC1, however, promoted a change in these dependencies; DAXX became a strong activator of itself, as did SUMO, whilst DAXX also became a strong activator of SUMO. In the full model, SUMO was a weak activator of DAXX, whereas following the loss of HDAC1 it became a strong activator of DAXX.

Figure ‎4.3.4: Distribution of dependency alterations following HSP90 KO.

Loss of HSP90 promoted four new strong activator dependencies to emerge. In the full model, NCOA6 was ambivalent to itself and PRKDC, whilst PRKDC was ambivalent to itself. Loss of HSP90, however, changed all of these dependencies to strong activation. In the full model, PRKDC was a weak activator of NCOA6, whilst in the HSP90 KO model it became a strong activator of NCOA6.