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

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.

Fig 1. Illustration of orthogonal array experimental design with 128 runs and 25 factors.

projecting the design points onto any two-dimensional subspace, the projected points always form a full factorial (4 × 4) with 8 replications for each level of combination.

Fig 2. Timeseries and Main Effects of iTreg cells in gastric lymph node for Parameter v _T.

Fig 3. Four distinct patterns for iTreg cells in the LP.

Fig 4. Timeseries and main effects of iTreg cells in lamina propria for parameter a _T exhibits monotonic pattern.

Fig 5. Timeseries and main effects of Th17 cells in GLN for parameter p ₁₇ exhibits bell shaped pattern.

Fig 6. Timeseries and main effects of iTreg cells in LP for parameter i _r exhibits sigmoid pattern.

Fig 7. Timeseries and main effects of iTreg cells in the GLN for parameter μ _CE exhibits complex pattern.

Fig 8. Overall p-values of modeling parameters on cell subsets in the synthetic gastric mucosa.

Fig 9. Temporal effects of parameters a _T, p ₁₇ and v _EC on output cells type per week.

Fig 10. Effect of parameter p ₁₇ on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.

Fig 11. Effect of parameter v _EC on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.

Fig 12. Effect of parameter μ _M1 on Th1, Th17, iTreg, M0, M1, and M2 phenotypes.