Fig 1.
Flow chart demonstrating the workflow of GEB052 model construction and analysis.
Database files were downloaded from the STRING website and interactions for proteins of interest were extracted. Extensive manual curation of predicted interactions was performed via literature searching, and the model was linked to biological outputs (cell death and inflammation) through manual curation of Gene Ontology records. CellNetAnalyzer (CNA) and the Signal Transduction Score Flow Algorithm (STSFA) were used for model analysis, with model predictions being verified via microarray data. The dashed line from Model Validation to The GEB052 Model represents validation and potential model refinement through assessment of model predictions.
Fig 2.
Nodes are represented by small blue circles, with the exception of the Input Node (GC) which is a green circle. The red circle represents the central node (the GR). Cell death and inflammation, the two model outputs, are shown in blue squares. Inhibitory edges are shown as red closed arrows whilst activation edges are shown as green open arrows.
Fig 3.
Interaction matrix for GEB052 model.
Figure adapted from the CNA-generated interaction matrix. The right-hand y-axis shows the number of reactions that each node is involved in, whilst the left-hand y axis shows the nodes present within the model. For the right-hand axis, numbers in brackets are equal to the number of nodes it activates, the number of nodes it inhibits, and the number of nodes it is regulated by. All model nodes for all model edges are assigned a value in the interaction matrix. Black is equivalent to no participation, whilst blue means the node is affected (i.e. regulated) by the interaction. Red means the node has an inhibition input whilst green means the node has a stimulatory input.
Fig 4.
Node connectivity of GEB052 model.
The number of edges interacting with the node is shown on the y-axis whilst the number of nodes with that degree of connectivity is shown on the x-axis.
Table 1.
Node connectivity of GEB052 model.
Fig 5.
Dependency matrix for GEB052 model.
Dependencies show the effect of the node on the y-axis on the node on the x-axis.
Table 2.
Dependency matrix alterations following in silico knockout analysis.
Fig 6.
Dependency alteration distribution following an in silico GR knockout.
This figure shows the alteration of dependencies following the removal of the GR node from the GEB052 model. Ambivalent dependencies are represented by a yellow circle, whilst weak activators and inhibitors are represented by a light green and pink circle respectively. The dark green circle represents strong activators, whilst the dark red circle represents strong inhibitors. No effect dependencies are represented by the dark grey circle.
Table 3.
Potentially novel predictions from dependency alterations.
Fig 7.
Visualisation of LSSA results from glucocorticoid-sensitive (A) and glucocorticoid-resistant (B) simulations. Nodes are coloured based on LSSA results: green indicates the node’s LSSA result was 1; orange indicates the node’s LSSA result was NaN and red indicates the node’s LSSA result was 0.
Table 4.
LSSA results for glucocorticoid-sensitive and glucocorticoid-resistant simulations.
Table 5.
Node state comparison from glucocorticoid-sensitive to glucocorticoid-resistant simulations.
Upregulated and downregulated refer to the fact that the node is more or less active in the glucocorticoid-resistant simulation than in the glucocorticoid-sensitive simulation.
Table 6.
Summary of prediction rates across all LSSA microarray validations.
Fig 8.
Clinical validation of GEB052 model via comparison of LSSA data to patient-based microarrays.
The “Patient Number” on the x-axis refers to the patient number used in the original study [31] that these patients were taken from. An asterisk (*) indicates that the p-value of correct predictions for that patient was statistically significant at p<0.05.
Table 7.
Prediction rates for the GEB052 model under STSFA analysis.
Fig 9.
Correct predictions of LSSA against STSFA.
Data represents the average correct prediction percentages +/- SEM. An asterisk (*) indicates p<0.05 as assessed by an unpaired t-test.
Fig 10.
Clinical validation of the GEB052 model under STSFA analysis.
The x-axis shows patient groups (Deceased at Risk Assessment, n = 1, Alive at Risk Assessment, n = 12) and the average for each group of the total edge weights targeting cell death +/- SEM are shown on the y-axis. Patient data taken from Schmidt and colleagues [31]and the GEO database.
Table 8.
Comparisons for genome-wide model validation.
Table 9.
Microarray data used for clinical validation of LSSA data.
Data taken from Schmidt and colleagues [31].
Table 10.
Patient microarray data used for STSFA analysis.
Data taken from Schmidt and colleagues [31].