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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.


Effect of parameter p17 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.
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pone.0136139.g010: Effect of parameter p17 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.

Mentions: Parameter p17 is the probability of resting T cell stimulation to Th17. The default value for the model is set at 0.1. We also observed the output for values of 0.25, 0.5, and 0.9. The experimental results are shown in Fig 10. When the value of p17 is increased, resting T cells are more likely to produce Th17Prolif than Th1Prolif. So, production of Th17 is increased. We can see that with increasing p17 we should create lower numbers of Th1 as compared to Th17. We also implemented transition of resting T to contact dependent transition. The transition depends on eDC and M1. As p17 does not directly affect M1 or eDC, we do not observe much change in the dynamics of M1 and eDC for p17. So, the effect of parameter p17 for the Th1, Th17 and iTreg dynamics are not very sensitive.


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)

Effect of parameter p17 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0136139.g010: Effect of parameter p17 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.
Mentions: Parameter p17 is the probability of resting T cell stimulation to Th17. The default value for the model is set at 0.1. We also observed the output for values of 0.25, 0.5, and 0.9. The experimental results are shown in Fig 10. When the value of p17 is increased, resting T cells are more likely to produce Th17Prolif than Th1Prolif. So, production of Th17 is increased. We can see that with increasing p17 we should create lower numbers of Th1 as compared to Th17. We also implemented transition of resting T to contact dependent transition. The transition depends on eDC and M1. As p17 does not directly affect M1 or eDC, we do not observe much change in the dynamics of M1 and eDC for p17. So, the effect of parameter p17 for the Th1, Th17 and iTreg dynamics are not very sensitive.

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.