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2015 ; 10
(9
): e0136139
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Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of
Immune Responses to Helicobacter pylori Infection
#MMPMID26327290
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[]; 10
(9
): e0136139
PMID26327290
show ga
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.
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