Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems

Kieran Alden*, Mark Read, Jon Timmis, Paul S. Andrews, Henrique Veiga-Fernandes, Mark Coles

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Citations (SciVal)

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.

Original languageEnglish
Article numbere1002916
JournalPLoS Computational Biology
Volume9
Issue number2
DOIs
Publication statusPublished - Feb 2013
Externally publishedYes

Keywords

  • Cell Movement
  • Computer Simulation
  • Lymphoid Tissue/growth & development
  • Models, Biological
  • Software

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