Schematic illustration of the process of applying ASPASIA to an SBML model.

<p>Using an initial SBML model as an input, ASPASIA generates a set of values as specified in a settings file, and new SBML files with parameters set to different values are created. Each model is then solved for sufficient time for a steady state to be reached and the resulting baseline values for all species supplied to ASPASIA. Then a new set of SBML files is created, and the same intervention, represented by a discrete alteration of a parameter or initial species concentration, added to all of them. The resulting files are then solved to steady state again and the effects of the intervention across the whole set of parameters and parameter values can be analysed. Black boxes represent processes that are performed only once, and white boxes represent processes that must be performed once for each model generated in step 3.</p

ASPASIA-generated model reflects observed biological behaviours of Th17-polarised CD4⁺ T cells.

<p>From 200 ASPASIA-generated models, a single model was selected that best captured biological behaviours. Shown are concentration of polarisating cytokines (left panels) and levels of transcription factor mRNA (right panels) in (A) the absence of type-1 polarising cytokines (C₁) and type-17 polarising cytokines (C₁₇), (B) following stimulation with C₁₇, and (C) following subsequent restimulation with C₁. Black lines represent C₁₇ (left panels) and ROR<i>γ</i>t (right panels), red dashed lines represent C₁ (left panels) and T-bet (right panels).</p

<i>In silico</i> experimentation reveals the parameters that control receptor X expression before and after polarisation with cytokines.

<p>ASPASIA was used to explore the sensitivity of the level of expression of the hypothetical receptor X following exposure to different cytokines. (A-C) Partial rank correlation coefficients (PRCC) for the correlation between receptor X expression and all parameters involved in phenotype switching were calculated for all models where receptor X acted to promote T-bet expression. PRCCs were calculated before polarisation (A), following polarisation with C₁₇ (B) and after C_X had been introduced (C). (D) PRCC for the correlation between the time taken for the phenotype switch to occur and all parameters involved in phenotype switching for the models where a phenotype switch took place. Details and definitions of all parameters are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005351#pcbi.1005351.s004" target="_blank">S3 Fig</a>.</p

Phenotype switch is robust to changes in concentration of cytokine X.

<p>Models were polarised to a Th17 state as previously described before 10 varying concentrations of cytokine X were simulated in order to drive a phenotype switch. (A) Dynamics of cytokine X in each of the 10 models. (B) Dynamics of ROR<i>γ</i>t (black) and T-bet (red, dashed) in each of the 10 models.</p

Experimentation using ASPASIA suggests that hypothetical receptor X drives phenotype switching in Th17 cells by promoting T-bet.

<p>(A) Representative profile of C₁₇ (black), C₁ (red, dashed) and C_X (blue, dashed) used to drive polarisation and phenotype switching. (B) Left panel: ROR<i>γ</i>t (black) and T-bet (red, dashed) expression in one representative model where C_X acts by inhibiting ROR<i>γ</i>t. Right panel: Number of models that have switched, or not switched under these conditions. (C) Left panel: ROR<i>γ</i>t (black) and T-bet (red, dashed) expression in a model with the same parameters as shown in left panel of B but with C_X acting by promoting T-bet resulting in a transition through a double-positive phase to an ex-Th17 state. Right panel: Number of models that have switched, or not switched under these conditions.</p

Additional file 3: of A computational framework for complex disease stratification from multiple large-scale datasets

Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kappa values of the Random Forest models in the training set. Table S9. Performances values for the Random Forest model in the testing set. Figure S11. Relative importance of the top 20 predictors building the final model of the RF. The importance axis is scaled, with the mRNA expression of CD3D scaled to 100% and the methylation state of POLA2 to 0% (not shown). (DOCX 18 kb

Additional file 4: of A computational framework for complex disease stratification from multiple large-scale datasets

DIABLO sPLSDA model results. (DOCX 18966 kb