Understanding and Treating Mycobacterium tuberculosis Infection: A Multi-Scale Modeling Approach.

Tuberculosis (TB), caused by the pathogen Mycobacterium tuberculosis (Mtb), remains a significant burden on global health. Central to both host immune responses and antibiotic treatment are structures known as granulomas. In this dissertation we used computational and experimental approaches at a single granuloma level to understand how immune responses to Mtb contribute to both bacterial control and persistence. In addition, we predicted the dynamics of antibiotics in granulomas and designed improved treatment strategies. We built a hybrid multi-scale model of Mtb infection that integrates the cytokines tumor necrosis factor-α (TNF) and interleukin-10 (IL-10). We predicted that a balance of TNF and IL-10 is essential to infection control with minimal host-induced tissue damage. We extended our description of TNF and IL-10 to include simplified models of intracellular signaling driving macrophage polarization, which suggests that the temporal dynamics of macrophage polarization in granulomas are predictive of granuloma outcome. Next, we focused on determining the role of IL-10 in controlling antimicrobial activity. We predicted a transient role for IL-10 in controlling a trade-off between early host immunity antimicrobial responses and tissue damage. This trade-off determines sterilization of granulomas. Lastly, using an experimental model of granuloma formation, we measured significant gradients of TNF in granulomas. xxii We developed a pharmacokinetic and pharmacodynamic model of oral dosing of rifampin and isoniazid used to treat Mtb and incorporated it into our computational model. We predicted that oral antibiotic strategies fail due to sub-optimal exposure in granulomas, which leads to bacterial regrowth between doses. We extended our platform to include a description of inhaled formulations dosed to the lungs with reduced frequencies. We predicted that dosing every two-weeks with an inhaled formulation of isoniazid is feasible with increased sterilization capabilities and reduced toxicity, while an inhaled formulation of rifampin has equivalent sterilization capabilities, but early associated toxicity and infeasible carrier loadings. The keys to understanding immune responses and successful antibiotic treatment of TB lie in the dynamics at the site of infection. Our results help identify the roles of cytokines during Mtb infection, provide new possibilities for immune related therapies, and guide design of better antibiotic strategies.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/108883/1/ncilfone_1.pd

Unconventional machine learning of genome-wide human cancer data

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences

Quantitative Systems Pharmacology Approaches Applied to Microphysiological Systems (MPS): Data Interpretation and Multi-MPS Integration

Our goal in developing Microphysiological Systems (MPS) technology is to provide an improved approach for more predictive preclinical drug discovery via a highly integrated experimental/computational paradigm. Success will require quantitative characterization of MPSs and mechanistic analysis of experimental findings sufficient to translate resulting insights from in vitro to in vivo. We describe herein a systems pharmacology approach to MPS development and utilization that incorporates more mechanistic detail than traditional pharmacokinetic/pharmacodynamic (PK/PD) models. A series of studies illustrates diverse facets of our approach. First, we demonstrate two case studies: a PK data analysis and an inflammation response––focused on a single MPS, the liver/immune MPS. Building on the single MPS modeling, a theoretical investigation of a four-MPS interactome then provides a quantitative way to consider several pharmacological concepts such as absorption, distribution, metabolism, and excretion in the design of multi-MPS interactome operation and experiments.United States. Defense Advanced Research Projects Agency. Microphysiological Systems Program (W911NF-12-2-0039)National Institutes of Health (U.S.) Microphysiological Systems Program (4-UH3-TR000496-03)Massachusetts Institute of Technology. Center for Environmental Health Sciences (NIEHS Grant P30-ES002109

Time course simulation results for baseline and IL-10 knockout scenarios.

<p>Simulation using baseline containment parameter set at 200 days post-infection. B. Simulation using the IL-10 knockout parameter set at 200 days post-infection. Agents and bacteria colors are as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone-0068680-g003" target="_blank">Figure 3</a>. C–F. Simulation results at 50, 100, 150 and 200 days post-infection using the baseline containment parameter set (black bars) and the IL-10 knockout parameter set (white bars). The few simulations that lead to clearance of <i>Mtb</i> in a granuloma are not shown here (see Table S10 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s004" target="_blank">Appendix S4</a>). C. Total bacterial load. D. Number of activated Mφ. E. Number of apoptotic resting Mφ. F. Average tissue concentration of TNF-α (pM). For full length time-lapse simulations please see <a href="http://malthus.med.micro.umich.edu/lab/movies/TNF-IL10" target="_blank">http://malthus.med.micro.umich.edu/lab/movies/TNF-IL10</a>.</p

Model validation of simulated granulomas at 200 days post-infection.

<p>A. Simulation using baseline containment parameter set. B. Simulation using TNF-α knockout parameter set (baseline containment parameter set but with k_{RNA_Mac} = 0 and k_{RNA_Tcell} = 0). C. Interferon-γ knockout parameter set (baseline containment parameter set P_STAT1 = 0). Cell types are as follows: resting macrophages (resting Mφ), infected macrophages (infected Mφ), chronically infected macrophage (chronic Mφ), activated macrophage (activated Mφ), pro-inflammatory T cell (T_γ), cytotoxic T cell (T_c), regulatory T cell (T_r), and extracellular bacteria (B_ext). Agent and bacteria colors are shown in the included legend. These same colors are used for subsequent images. Model parameters are given in Table S3 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a>, Table S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a>, and Table S5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a>. For full length time-lapse simulations please see <a href="http://malthus.med.micro.umich.edu/lab/movies/TNF-IL10" target="_blank">http://malthus.med.micro.umich.edu/lab/movies/TNF-IL10</a>.</p

Altering the ratio of [TNF-α]/[IL-10] in a granuloma environment.

<p>Simulation results at 200 days post-infection showing the effects of altering the ratio of average tissue concentrations of TNF-α to IL-10 in the granuloma environment ([TNF-α]/[IL-10]). A total of 296 simulations (4 replications) were performed yielding various values of [TNF-α]/[IL-10]. Comparison of the ratio of concentrations of TNF-α to IL-10 with: A. B. C. Total bacterial load, number of activated Mφ, and number of apoptotic resting Mφ as a function of [TNF-α]/[IL-10]. D. Host-Pathogen Index (H.P.I), a metric that combines the three previous measures as a function of [TNF-α]/[IL-10]. The green star is the average simulation result for the baseline containment parameter set. E–G. Representative granuloma snapshots at 200 days post-infection for each of the regions (1, 2, and 3) defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone-0068680-g007" target="_blank">Figure 7D</a>. For full length time-lapse simulations please see <a href="http://malthus.med.micro.umich.edu/lab/movies/TNF-IL10" target="_blank">http://malthus.med.micro.umich.edu/lab/movies/TNF-IL10</a>.</p

Schematic representation of the hybrid multi-scale ABM of the immune response to <i>Mtb</i>.

<p>A. An overview of GranSim with a sub-section of model rules shown that represents known immune cell behaviors and interactions (Adapted from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680-FallahiSichani1" target="_blank">[13]</a>). A full list of rules is available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s001" target="_blank">Appendix S1</a> B. Schematic representation of single cell-level TNF-α and IL-10 binding and trafficking reactions. Model equations are shown in Table S1 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s002" target="_blank">Appendix S2</a> and Table S2 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s002" target="_blank">Appendix S2</a>.</p

Three main processes influence the concentrations of TNF-α and IL-10 and control infection outcome.

<p>Model parameters that are relevant to TNF-α or IL-10 synthesis (synthesis influence), that control the spatial distribution of TNF-α or IL-10 (spatial influence), and that control the binding and signaling of TNF-α or IL-10 (signaling influence). These three parameter groups control the concentrations of TNF-α and IL-10 in the granuloma environment and thus in turn directly control infection outcome. Parameter groups are described in Table S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a> and Table S5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a>.</p

Simulation results showing the effects of varying each influence in a granuloma environment.

<p>Simulation results from 30 replications showing the effects of varying each of the three influences: the synthesis influence, signaling influence, and spatial influence (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone-0068680-g005" target="_blank">Figure 5</a>; Table S4 and Table S5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a>). Results using the baseline containment parameter set, labeled ‘base’, are included for comparison (parameter values in Table S4 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a> and Table S5 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0068680#pone.0068680.s003" target="_blank">Appendix S3</a>). A. Effects of mRNA synthesis rate of TNF-α by Mφ (‘Low’ <i>k_{RNA_Mac}</i> = 0.5 #/cell*s, ‘High’ <i>k_{RNA_Mac}</i> = 3.0 #/cell*s) and synthesis rate of IL-10 by activated Mφ (‘Low’ <i>k_synthMacAct</i> = 0.1 #/cell*s, ‘High’ <i>k_synthMacAct</i> = 1.0 #/cell*s). B. Effect of TNFR1 receptor density on Mφ (‘Low’ <i>TNFR1_mac</i> = 500, ‘High’ <i>TNFR1_mac</i> = 5000) and IL-10R receptor density on Mφ (‘Low’ <i>IL10R_mac</i> = 500, ‘High’ <i>IL10R_mac</i> = 5000). C. Effect of bound TNFR1 internalization rate constant (‘Low’ <i>k_int1</i> = 10⁻⁴ s⁻¹, ‘High’ <i>k_int1</i> = 10⁻³ s⁻¹) and bound IL-10R internalization rate constant (‘Low’ <i>k_int</i> = 10⁻⁴ s⁻¹, ‘High’ <i>k_int</i> = 10⁻³ s⁻¹). D. Effect of spatial range of TNF-α (‘Low’ <i>D_TNF</i> = 1×10⁻⁸ cm²/s <i>k_deg</i> = 2.3×10⁻² s⁻¹, ‘High’ <i>D_TNF</i> = 9×10⁻⁸ cm²/s <i>k_deg</i> = 5×10⁻⁵ s⁻¹) and spatial range of IL-10 (‘Low’ <i>D_IL10</i> = 1×10⁻⁸ cm²/s <i>k_deg</i> = 1.6×10⁻² s⁻¹, ‘High’ <i>D_IL10</i> = 8×10⁻⁸ cm²/s <i>k_deg</i> = 1.8×10⁻⁶ s⁻¹). Low indicates a lower value than baseline while high indicates a higher value than baseline.</p

Schematic diagram of TNF-α and IL-10 mechanisms included in GranSim.

<p>Regulatory T cells, activated macrophages, infected macrophages, and chronically infected macrophages are able to produce IL-10. IL-10 inhibits the production of TNF-α in all cell types. IL-10 indirectly prevents the recruitment of immune cells to the site of infection by inhibiting chemokine production. IL-10 limits the secondary regulatory mechanism (cell-cell contact, TGF-β, and other regulatory mechanisms) down regulation of activated macrophages by regulatory T cells. Activated macrophages, infected macrophages, chronically infected macrophages, resting macrophages (STAT1 or NFκB activated), cytotoxic T cells, and pro-inflammatory T cells are able to produce TNF-α. TNF-α directly induces recruitment of immune cells to the site of infection (lung). TNF-α induces production of IL-10 in activated macrophages, which represents the pro/anti inflammatory plasticity of activated macrophages. TNF-α, along with interferon-γ derived from pro-inflammatory T cells, induces activation of resting macrophages or it can induce the caspase-mediated apoptosis pathway found in all cell types.</p