Abstract
Integrating computer simulation with conventional wet-lab research has proven to have much potential in furthering the understanding of biological systems. Success requires the relationship between simulation and the real-world system to be established: substantial aspects of the biological system are typically unknown, and the abstract nature of simulation can complicate interpretation of in silico results in terms of the biology. Here we present spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a package of statistical techniques specifically designed to help researchers understand this relationship and provide novel biological insight. The tools comprising spartan help identify which simulation results can be attributed to the dynamics of the modelled biological system, rather than artefacts of biological uncertainty or parametrisation, or simulation stochasticity. Statistical analyses reveal the influence that pathways and components have on simulation behaviour, offering valuable biological insight into aspects of the system under study. We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation. Spartan is released under a GPLv2 license, implemented within the open source R statistical environment, and freely available from both the Comprehensive R Archive Network (CRAN) and http://www.cs.york.ac.uk/spartan. The techniques within the package can be applied to traditional ordinary or partial differential equation simulations as well as agent-based implementations. Manuals, comprehensive tutorials, and example simulation data upon which spartan can be applied are available from the website.
Highlights
The integration of computer simulation with current experimental techniques has become a popular approach to aid the understanding of biological systems [1]
Such simulation results may be affected by uncertainty arising from aspects of the biological system that are currently unknown and need to be assumed, and by uncertainty introduced in the implementation of the simulator [5]
We have developed spartan (Simulation Parameter Analysis R Toolkit ApplicatioN), a toolkit of statistical techniques that aid understanding and analysis of results generated through simulation
Summary
The integration of computer simulation with current experimental techniques has become a popular approach to aid the understanding of biological systems [1]. The package provides implementations of previously described statistical analysis techniques [6,7,13] that when brought together provide a comprehensive toolkit to explore the effect uncertainty has on simulation results Such uncertainty may be present in two forms: aleatory uncertainty that arises through stochasticity inherent in both the biological and simulated systems, and epistemic uncertainty reflecting simulation parameters for which a value has yet to be or cannot be determined as the biological understanding is incomplete [5]. We demonstrate the use of spartan in an exploration of results generated from this simulator: to determine the number of simulation samples required to mitigate stochastic effects and attain a desired level of experimental accuracy, build confidence that our results are representative of biology as opposed to parameterisation artefacts resulting from epistemic uncertainty, and gain valuable biological insight through rigorous statistical analysis of simulation results. Whereas our previous work highlights the need to adhere to a robust method of simulation design and development informed by wet-lab research, our case study shows how spartan can provide a mechanism to integrate simulation results back into wet-lab research
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