ASPASIA Published in PLoS Computational Biology

After two years of working on this project and manuscript revisions alongside Dr Stephanie Evans, I am pleased to say that our paper describing our new software tool, ASPASIA, has now been published in PLoS Computational Biology.

ASPASIA was developed during Steph’s PhD rotation at GSK, where she identified a deficiency in tools available for analysing interventions applied to models specified in Systems Biology Markup Language (SBML). We developed a Java based tool to address these deficiencies and provide a new platform for performing sensitivity analysis of SBML models that are dependent on capturing a biological intervention. Use of the tool is demonstrated in the paper through a case study that examines the mechanisms by which Th17-cell plasticity may be controlled in vivo.

The tool is available from the York Computational Immunology Lab website.

Paper Abstract:

A calibrated computational model reflects behaviours that are expected or observed in a complex system, providing a baseline upon which sensitivity analysis techniques can be used to analyse pathways that may impact model responses. However, calibration of a model where a behaviour depends on an intervention introduced after a defined time point is difficult, as model responses may be dependent on the conditions at the time the intervention is applied. We present ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis), a cross-platform, open-source Java toolkit that addresses a key deficiency in software tools for understanding the impact an intervention has on system behaviour for models specified in Systems Biology Markup Language (SBML). ASPASIA can generate and modify models using SBML solver output as an initial parameter set, allowing interventions to be applied once a steady state has been reached. Additionally, multiple SBML models can be generated where a subset of parameter values are perturbed using local and global sensitivity analysis techniques, revealing the model’s sensitivity to the intervention. To illustrate the capabilities of ASPASIA, we demonstrate how this tool has generated novel hypotheses regarding the mechanisms by which Th17-cell plasticity may be controlled in vivo. By using ASPASIA in conjunction with an SBML model of Th17-cell polarisation, we predict that promotion of the Th1-associated transcription factor T-bet, rather than inhibition of the Th17-associated transcription factor ROR?t, is sufficient to drive switching of Th17 cells towards an IFN-?-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour. ASPASIA, released under the Artistic License (2.0), can be downloaded from