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Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.

Alden K, Read M, Timmis J, Andrews PS, Veiga-Fernandes H, Coles M - PLoS Comput. Biol. (2013)

Bottom Line: 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.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.We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation.

View Article: PubMed Central - PubMed

Affiliation: Centre for Systems Biology, School of Biosciences, University of Birmingham, Birmingham, United Kingdom. k.j.alden@bham.ac.uk

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 RToolkit 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.

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Related in: MedlinePlus

Use of spartan to identify compound effects between parameters.A: Parameter that captures the chemoattractant expression level required to influence cell motility. No trend or effects are apparent. B: Parameter which captures the level of adhesion required to restrict cell motility. A clear trend is apparent suggesting this has a large influence on simulated cell behaviour.
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pcbi-1002916-g003: Use of spartan to identify compound effects between parameters.A: Parameter that captures the chemoattractant expression level required to influence cell motility. No trend or effects are apparent. B: Parameter which captures the level of adhesion required to restrict cell motility. A clear trend is apparent suggesting this has a large influence on simulated cell behaviour.

Mentions: For online tutorial 3, spartan produces the plots in Figure 3. These detail cell velocity responses for two parameters. In Figure 3(b), detailing the effect of cellular adhesion, a clear trend emerges. The correlation coefficient reveals this parameter's significant influence on the simulation response. The same conclusions cannot be drawn for the parameter in Figure 3(a), where no trend emerges. Whereas the previous technique identified initial chemokine expression as an influential factor when the parameters where perturbed individually, this result suggests adhesion factor expression is the highly influential pathway at this time-point. Such a prediction could be verified experimentally through examining cell behaviour when expression of adhesion factors are blocked.


Spartan: a comprehensive tool for understanding uncertainty in simulations of biological systems.

Alden K, Read M, Timmis J, Andrews PS, Veiga-Fernandes H, Coles M - PLoS Comput. Biol. (2013)

Use of spartan to identify compound effects between parameters.A: Parameter that captures the chemoattractant expression level required to influence cell motility. No trend or effects are apparent. B: Parameter which captures the level of adhesion required to restrict cell motility. A clear trend is apparent suggesting this has a large influence on simulated cell behaviour.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585389&req=5

pcbi-1002916-g003: Use of spartan to identify compound effects between parameters.A: Parameter that captures the chemoattractant expression level required to influence cell motility. No trend or effects are apparent. B: Parameter which captures the level of adhesion required to restrict cell motility. A clear trend is apparent suggesting this has a large influence on simulated cell behaviour.
Mentions: For online tutorial 3, spartan produces the plots in Figure 3. These detail cell velocity responses for two parameters. In Figure 3(b), detailing the effect of cellular adhesion, a clear trend emerges. The correlation coefficient reveals this parameter's significant influence on the simulation response. The same conclusions cannot be drawn for the parameter in Figure 3(a), where no trend emerges. Whereas the previous technique identified initial chemokine expression as an influential factor when the parameters where perturbed individually, this result suggests adhesion factor expression is the highly influential pathway at this time-point. Such a prediction could be verified experimentally through examining cell behaviour when expression of adhesion factors are blocked.

Bottom Line: 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.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.We demonstrate the power of spartan in providing critical insight into aspects of lymphoid tissue development in the small intestine through simulation.

View Article: PubMed Central - PubMed

Affiliation: Centre for Systems Biology, School of Biosciences, University of Birmingham, Birmingham, United Kingdom. k.j.alden@bham.ac.uk

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 RToolkit 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.

Show MeSH
Related in: MedlinePlus