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Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

Alam M, Deng X, Philipson C, Bassaganya-Riera J, Bisset K, Carbo A, Eubank S, Hontecillas R, Hoops S, Mei Y, Abedi V, Marathe M - PLoS ONE (2015)

Bottom Line: Determining the key modeling parameters which govern the outcomes of the system is very challenging.In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system.We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

View Article: PubMed Central - PubMed

Affiliation: Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America.

ABSTRACT
Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

No MeSH data available.


Timeseries and Main Effects of iTreg cells in gastric lymph node for Parameter vT.
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pone.0136139.g002: Timeseries and Main Effects of iTreg cells in gastric lymph node for Parameter vT.

Mentions: ENISI provides the total number of agents per phenotype in each time step as the system output. Typically these output are represented as time series plots, where the x and y axes represent time step and the number of cells respectively. To quantify the influence of any parameter on the model output, we generated time series plots for each input parameter. The time series plot for a parameter has four curves, each belonging to a distinct parameter Level. As shown in the design diagram, a total of 128 experiments with different parameter settings were performed, where each experiment was replicated 15 times. According to the experimental design, each parameter has 32 distinct experiments per level. Hence, each curve of the time series plot has 32 × 15 = 480 simulation runs. The average of the runs are drawn along with the associated error bars in each curve. For example, Fig 2(a) shows the time series curves (along with error-bars) of iTreg cells in the gastric lymph node (GLN) for modeling parameter vT.


Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

Alam M, Deng X, Philipson C, Bassaganya-Riera J, Bisset K, Carbo A, Eubank S, Hontecillas R, Hoops S, Mei Y, Abedi V, Marathe M - PLoS ONE (2015)

Timeseries and Main Effects of iTreg cells in gastric lymph node for Parameter vT.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556515&req=5

pone.0136139.g002: Timeseries and Main Effects of iTreg cells in gastric lymph node for Parameter vT.
Mentions: ENISI provides the total number of agents per phenotype in each time step as the system output. Typically these output are represented as time series plots, where the x and y axes represent time step and the number of cells respectively. To quantify the influence of any parameter on the model output, we generated time series plots for each input parameter. The time series plot for a parameter has four curves, each belonging to a distinct parameter Level. As shown in the design diagram, a total of 128 experiments with different parameter settings were performed, where each experiment was replicated 15 times. According to the experimental design, each parameter has 32 distinct experiments per level. Hence, each curve of the time series plot has 32 × 15 = 480 simulation runs. The average of the runs are drawn along with the associated error bars in each curve. For example, Fig 2(a) shows the time series curves (along with error-bars) of iTreg cells in the gastric lymph node (GLN) for modeling parameter vT.

Bottom Line: Determining the key modeling parameters which govern the outcomes of the system is very challenging.In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system.We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

View Article: PubMed Central - PubMed

Affiliation: Network Dynamics and Simulation Science Laboratory, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Center for Modeling Immunity to Enteric Pathogens, Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, Virginia, United States of America; Department of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America.

ABSTRACT
Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

No MeSH data available.