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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 μM1 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.
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pone.0136139.g012: Effect of parameter μM1 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.

Mentions: Parameter μM1 represents the ability for pro-inflammatory M1 cells to eliminate bacteria. The default value of μM1 is 1. To observe the effect of μM1 on the system, the value was changed to 0.5, 0.25 and 0.0 in decreasing order such that fewer bacteria are killed. The ability for macrophages to rid the system of H. pylori caused the most noticeable changes in a peak of lamina propria myeloid cells (M0) between days 10 and 22 (Fig 12). More than a 10-day delay occurs in the M0 population when μM1 from is shifted from 0.25 to 0.5. Additionally, at the higher killing capacity (μM1 = 0.5) bacteria is cleared more rapidly but causes more significant inflammation when compared to μM1 = 0.25. When μM1 is set to its default value, the myeloid population peaks between days 15 and 16 and prevents an overzealous cellular response.


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 μM1 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.g012: Effect of parameter μM1 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.
Mentions: Parameter μM1 represents the ability for pro-inflammatory M1 cells to eliminate bacteria. The default value of μM1 is 1. To observe the effect of μM1 on the system, the value was changed to 0.5, 0.25 and 0.0 in decreasing order such that fewer bacteria are killed. The ability for macrophages to rid the system of H. pylori caused the most noticeable changes in a peak of lamina propria myeloid cells (M0) between days 10 and 22 (Fig 12). More than a 10-day delay occurs in the M0 population when μM1 from is shifted from 0.25 to 0.5. Additionally, at the higher killing capacity (μM1 = 0.5) bacteria is cleared more rapidly but causes more significant inflammation when compared to μM1 = 0.25. When μM1 is set to its default value, the myeloid population peaks between days 15 and 16 and prevents an overzealous cellular response.

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.