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

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  • Kieran Alden
  • Mark Read
  • Jon Timmis
  • Paul S Andrews
  • Henrique Veiga-Fernandes
  • Mark Coles

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.

Suggested Citation

  • Kieran Alden & Mark Read & Jon Timmis & Paul S Andrews & Henrique Veiga-Fernandes & Mark Coles, 2013. "Spartan: A Comprehensive Tool for Understanding Uncertainty in Simulations of Biological Systems," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-9, February.
    • Handle: RePEc:plo:pcbi00:1002916
    DOI: 10.1371/journal.pcbi.1002916
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    1. Tarantola, S. & Gatelli, D. & Mara, T.A., 2006. "Random balance designs for the estimation of first order global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 717-727.
    2. Saltelli, Andrea & Bolado, Ricardo, 1998. "An alternative way to compute Fourier amplitude sensitivity test (FAST)," Computational Statistics & Data Analysis, Elsevier, vol. 26(4), pages 445-460, February.
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    1. Russell, Robin E. & Walsh, Daniel P. & Samuel, Michael D. & Grunnill, Martin D. & Rocke, Tonie E., 2021. "Space matters: host spatial structure and the dynamics of plague transmission," Ecological Modelling, Elsevier, vol. 443(C).
    2. Mark N Read & Jacqueline Bailey & Jon Timmis & Tatyana Chtanova, 2016. "Leukocyte Motility Models Assessed through Simulation and Multi-objective Optimization-Based Model Selection," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-34, September.
    3. Brinkmann, Katja & Kübler, Daniel & Liehr, Stefan & Buerkert, Andreas, 2021. "Agent-based modelling of the social-ecological nature of poverty traps in southwestern Madagascar," Agricultural Systems, Elsevier, vol. 190(C).

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