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.

Agent‐based models of inflammation in translational systems biology: A decade later

“…In addition to the ability to facilitate the development and use of increasingly sophisticated ABMs, there have also been methodological improvements for both improving ABMs as well as analyzing the output of simulation experiments utilizing them (Figure ). These developments include work on uncertainty quantification in ABMs (Marino, Hogue, Ray, & Kirschner, ), sensitivity analysis in ABMs (Alam et al, ), methods for increasing the computational efficiency of ABMs via “tuneable resolution” (Kirschner, Hunt, Marino, Fallahi‐Sichani, & Linderman, ), the use of Bayesian statistical model checking for parameter estimation in ABMs (Hussain et al, ), the use of optimization algorithms in conjunction with ABMs (Cicchese, Pienaar, Kirschner, & Linderman, ; R. C. Cockrell & An, ), the use of HPC (C. Cockrell & An, ; R. C. Cockrell & An, ; R. C. Cockrell et al, ; Petersen et al, ; Seekhao et al, ), strategies for data‐driven model validation (Renardy et al, ), and the incorporation of model‐based dynamic control discovery (R. C. Cockrell & An, ; Petersen et al, ). These are exciting developments that have, without a doubt, increased the range of biomedical problems and applications to which ABMs could be applied.…”

“…In addition to the ability to facilitate the development and use of increasingly sophisticated ABMs, there have also been methodological improvements for both improving ABMs as well as analyzing the output of simulation experiments utilizing them ( Figure 1). These developments include work on uncertainty quantification in ABMs (Marino, Hogue, Ray, & Kirschner, 2008), sensitivity analysis in ABMs (Alam et al, 2015), methods for increasing the computational efficiency of ABMs via "tuneable resolution" (Kirschner, Hunt, Marino, Fallahi-Sichani, & Linderman, 2014), the use of Bayesian statistical model checking for parameter estimation in ABMs (Hussain et al, 2015), the use of optimization algorithms in conjunction with ABMs (Cicchese, Pienaar, Kirschner, & Linderman, 2017;R. C. Cockrell & An, 2018), the use of HPC (C. Cockrell & An, 2017; R. C. Cockrell & An, 2018;R.…”

Host‐microbe interaction in the gastrointestinal tract

“…The analysis of this type of system dynamics has been carried out using computational modelling techniques (Christley et al, 2015). Such computational models have been used to describe the spatiotemporal interactions between pathogens, T cells, macrophages, dendritic cells and epithelial cells during infection (Wendelsdorf et al, 2012;Alam et al, 2015). Simulations of experimentally inaccessible scenarios have for instance predicted that the removal of neutrophils and epithelialderived anti-microbial compounds would enhance commensal bacteria growth and promote recovery against Clostridium difficile infection (Leber et al, 2015).…”

Complex Stability and an Irrevertible Transition Reverted by Peptide and Fibroblasts in a Dynamic Model of Innate Immunity

“…Parameter sensitivity analysis can then be used to establish how readily the network function becomes critical as a parameter is changed. In the context of immunology, sensitivity analysis has, for instance, been used to evaluate the influence of maternal adaptive immunity on the time dependence of infection and on its consequences for serology (44) to identify the relative importance of the molecular components in coupled MAPK and PI3K signal transduction pathways (45), for the NF-κB signaling network (46), in an immune-based model of Helicobacter pylori infection (47), and in analyzing the T cell response to antigen (48). An approach related to sensitivity analysis is metabolic control analysis, which is limited to sensitivities with respect to process activities (49)(50)(51).…”