I am the lead author of two software tools that help in the analysis of models of biological systems, and was heavily involved in the development of a third:
Successful integration of computer simulation with wet-lab research requires the relationship between simulation and the real-world system to be established. Spartan, described in our paper in PLoS Computational Biology, is a package of statistical techniques specifically designed to understand this relationship and provide novel biological insight. These techniques help reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study.
Spartan is open source, implemented within the R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and on the York Computational Immunology Lab website. Use of the package is demonstrated via the tutorial published in the R Journal, and is accompanied by example simulation data for each described technique.
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. ASPASIA (Automated Simulation Parameter Alteration and SensItivity Analysis) is 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). This tool was described in our recent paper in PLoS Computational Biology, and is available from the York Computational Immunology Lab website.
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. In our upcoming PLoS Computational Biology paper (in press), we demonstrate how ASPASIA 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. This approach can be applied to all SBML-encoded models to predict the effect that intervention strategies have on system behaviour.
Within YCIL we developed Artoo to enable the construction of logical arguments to support the engineering decisions that underlie our simulations, taking inspiration from the development of safety-critical software systems. This tool enables you to build and manipulate a graphical structure consisting of text-containing nodes and arrows that represents your argument. Specifically, it uses the syntax of the Goal Structuring Notation. Structures can be saved to file and exported as PNG images. A publication describing ARTOO was published in Royal Society Interface and followed up by a tutorial in CPT: Pharmacometrics & Systems Pharmacology. Artoo itself is now maintained and developed further by SimOmics Ltd.