<i>In silico</i> simulation of altered commensal bacteria regrowth during <i>Clostridium difficile</i> infection.

<p>Four cases were tested with variations to the inhibition of the commensal bacteria growth: inhibited by both neutrophils and inflamed epithelial cells (N and E_i), by only neutrophils (N), by only inflamed epithelial cells (E_i), and by neither (none). Resulting changes in species populations for each case are shown: (a) <i>baiCD</i>-containing commensal species, (b) <i>C</i>. <i>difficile</i>, (c) activated neutrophils, and (d) iTreg cells in the lamina propria.</p

Commensal bacteria regrowth inhibited with <i>Clostridium difficile</i> infection.

<p>(a) The <i>baiCD</i> content is decreased by antibiotic treatment in both control and <i>C</i>. <i>difficile</i> challenged mice and further decreased post-infection in the <i>C</i>. <i>difficile</i> challenged mice compared to the controls. (b and c) The expression of anti-microbial peptides DefB1 and S100A8 are upregulated with infection. Data points and error bars represent mean ± standard error of the mean (SEM). Asterisks (*) mark significance (p≤0.05) in comparison between control and <i>C</i>. <i>difficile</i> infected mice (n = 10).</p

Simulated dynamics of mucosal immune response to <i>Clostridium difficile</i>.

<p>Modeling results following calibration and validation of the host response model in populations of (a) <i>C</i>. <i>difficile</i>, (b) protective commensal bacteria, (c) infection-exacerbating commensal bacteria, (d) lamina propria T helper 17 cells, (e) effector dendritic cells, (f) infiltrating neutrophils, (g) regulatory T cells, (h) tolerogenic dendritic cells and (i) activated macrophages. Lines represent simulation results, filled points represent experimental calibration data and unfilled points represent experimental validation data.</p

Relative effects of parameters on <i>Clostridium difficile</i> population and epithelial cell death.

<p>(a and b) Histograms showing the distribution of parameter impact on <i>C</i>. <i>difficile</i> population and epithelial cell death, respectively. Measurements are based on sensitivity analysis of the calibrated model. (c and d) Highest impact parameters for each quantity in which positive amounts indicate an increasing effect on the quantity and negative amounts indicate a decreasing effect. For the <i>C</i>. <i>difficile</i> population results, P1 contributes to effector dendritic cell production, P2 to neutrophil activation and migration, P3 to protective commensal bacteria regrowth, P4 to macrophage activation, P5 to commensal bacteria death, P6 to macrophage death, P7 to Th17 cell death, and P8 to neutrophil death. For epithelial cell death, P1 contributes to tolerogenic dendritic cell production, P2 to Th17 to Treg cell plasticity, P3 to commensal bacteria death, P4 to Treg to Th17 cell plasticity, P5 to macrophage activation and P6 to <i>C</i>. <i>difficile</i> growth.</p

Time course of <i>Clostridium difficile</i> infection in mice.

<p>(a and b) Flow cytometry analysis of colonic lamina propria lymphocytes from days 1 to 10 post-infection showing the differences in CD4+ CD25+ FoxP3+ regulatory T (Treg) and CD4+ IL17+ T helper 17 (Th17) cells, respectively, between control and <i>C</i>. <i>difficile</i> challenged wild type mice. (c) Re-isolation data of <i>C</i>. <i>difficile</i> from colonic contents from day 1 to day 8 post-infection. Data points and error bars represent mean ± standard error of the mean (SEM). Asterisks (*) mark significance (<i>P</i>≤0.05) in comparison between control and <i>C</i>. <i>difficile</i> infected mice (n = 10).</p

Network topology of model illustrating mucosal immune responses to <i>Clostridium difficile</i>.

<p>Systems biology markup language (SBML) compliant network of interactions between <i>C</i>. <i>difficile</i> and cellular immune components created in CellDesigner. Reaction modifiers connect cell nodes to reaction arrows with green as indication of activation and red of inhibition. Species consist of <i>C</i>. <i>difficile</i> (<i>Cdiff</i>), infection-exacerbating commensal bacteria (<i>CommH</i>), protective commensal bacteria (<i>CommB</i>), dead commensal bacteria (<i>CommD</i>), epithelial cells (<i>E</i>), inflamed epithelial cells (<i>E</i>_<i>i</i>), neutrophils (<i>N</i>), macrophages (<i>M</i>), dendritic cells (<i>tDC</i> and <i>eDC</i>), T cells (<i>nT</i>, <i>Treg</i>, <i>Th17</i>, <i>Th1</i>) existing in multiple compartments lumen (<i>Lum</i>), epithelium (<i>EP</i>), lamina propria (<i>LP</i>), and mesenteric lymph node (<i>MLN</i>).</p

Effect of parameter <i>v</i>_<i>EC</i> on <i>Th</i>1, <i>Th</i>17, <i>iTreg</i>, <i>M</i>0, <i>M</i>1, and <i>M</i>2 phenotypes.

<p>Effect of parameter <i>v</i>_<i>EC</i> on <i>Th</i>1, <i>Th</i>17, <i>iTreg</i>, <i>M</i>0, <i>M</i>1, and <i>M</i>2 phenotypes.</p

Effect of parameter <i>p</i>₁₇ on <i>Th</i>1, <i>Th</i>17, <i>iTreg</i>, <i>M</i>0, <i>M</i>1, and <i>M</i>2 phenotypes.

<p>Effect of parameter <i>p</i>₁₇ on <i>Th</i>1, <i>Th</i>17, <i>iTreg</i>, <i>M</i>0, <i>M</i>1, and <i>M</i>2 phenotypes.</p

Effect of parameter <i>μ</i>_<i>M</i>1 on <i>Th</i>1, <i>Th</i>17, <i>iTreg</i>, <i>M</i>0, <i>M</i>1, and <i>M</i>2 phenotypes.

<p>Effect of parameter <i>μ</i>_<i>M</i>1 on <i>Th</i>1, <i>Th</i>17, <i>iTreg</i>, <i>M</i>0, <i>M</i>1, and <i>M</i>2 phenotypes.</p

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

<div><p>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 <i>Helicobacter pylori</i> colonization of the gastric mucosa.</p></div