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ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behavior.

Evans, Stephanie, Alden, Kieran, Cucurull-Sanchez, Lourdes, Larminie, Christopher, Coles, Mark C, Kullberg, Marika C and Timmis, Jonathan (2017) ASPASIA: A toolkit for evaluating the effects of biological interventions on SBML model behavior. PLOS Computational Biology, 13 (2). ISSN 1553-734X

Item Type: Article


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けt, is sufficient to drive switching of Th17 cells towards an IFN-け-producing phenotype. Our approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour.

journal.pcbi.1005351.pdf - Published Version
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Additional Information: ASPASIA, released under the Artistic License (2.0), can be downloaded from
Depositing User: Klaire Purvis-Shepherd


Item ID: 10847
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ISSN: 1553-734X
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Date Deposited: 07 Jun 2019 13:55
Last Modified: 30 Sep 2020 11:04


Author: Stephanie Evans
Author: Kieran Alden
Author: Lourdes Cucurull-Sanchez
Author: Christopher Larminie
Author: Mark C Coles
Author: Marika C Kullberg
Author: Jonathan Timmis

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