Tumor Necrosis Factor-Regulated Granuloma Formation in Tuberculosis: Multi-Scale Modeling and Experiments.

Tuberculosis is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). Multiple immune factors control host responses to Mtb infection, including the formation of granulomas in the lung, which are aggregates of bacteria, infected and uninfected immune cells whose function may reflect success or failure of the host to control infection. One such factor is tumor necrosis factor-α (TNF). TNF has been experimentally characterized to affect macrophage activation, apoptosis, chemokine and cytokine production during Mtb infection. Measurement of TNF concentrations and TNF activities within a granuloma to determine the relevant mechanisms for control of infection are difficult to assess in vivo. Further, processes that control TNF availability and activities within a granuloma remain unknown. We developed a multi-scale computational model that describes the immune response to Mtb in lung over three biological length scales: tissue, cellular and molecular. We used the results of sensitivity analysis as a tool to identify which experiments were needed to measure critical model parameters in an experimental system. This system is a model of a granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads. Using these parameters in the model, we identified processes that regulate TNF availability and cellular behaviors and thus influence the outcome of infection within a granuloma. At the level of TNF/TNF receptor dynamics, TNF receptor internalization kinetics were shown to significantly influence TNF concentration dynamics, macrophage and T cell recruitment to site of infection, macrophage activation and apoptosis. These processes play a critical role in control of inflammation and bacterial levels within a granuloma. At the level of intracellular signaling, our analysis elucidated intracellular NF-κB associated signaling molecules and processes that may be new targets for control of infection and inflammation. We also used the model to explain what mechanisms lead to clinically observed differential effects of TNF-neutralizing drugs (generally used to treat inflammatory diseases) on reactivation of tuberculosis. Ultimately, these results can help to elaborate relevant features of the immune response to Mtb infection, identifying new strategies for therapy and prevention of tuberculosis as well as for development of safer anti-TNF drugs to treat inflammatory diseases.Ph.D.Chemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91477/1/fallahi_1.pd

Quantifying the Generation of T Cell Immunity using a Systems Biology Approach.

The immune system is our defense against pathogens. Quantitatively predicting its response to foreign stimulation is key toward developing tools to interfere with or prevent infection (e.g. vaccines and immunotherapies). I use a systems biology approach and develop computational models describing dynamics occurring within lymph nodes, sites where activated immune cells are generated. These effector cells circulate out into blood and to sites of infection participating in immunity. I both quantitatively and qualitatively study dynamics of immune cells during a generalized infection as well as during infection with Mycobacterium tuberculosis (Mtb). The models predict that their 3-dimensional configuration enables the lymph nodes to support rare antigen-specific T cells to efficiently search for antigen-bearing dendritic cells, and this efficiency is not reduced when the precursor frequency increases in a wide range. The models also predict strategies to manipulate the differentiation of immune cells to maximize specific subtypes of T cells populations, depending on different immunomodulation goals. When coupled with Mtb infection models, our models are able to assist vaccine design by finding correlations between immune cell subsets and protection against Mtb, and also help identify mechanisms controlling different disease outcomes at host level.PhDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113563/1/changgon_1.pd

Tackling complexity in biological systems: Multi-scale approaches to tuberculosis infection

Tuberculosis is an ancient disease responsible for more than a million deaths per year worldwide, whose complex infection cycle involves dynamical processes that take place at different spatial and temporal scales, from single pathogenic cells to entire hosts' populations. In this thesis we study TB disease at different levels of description from the perspective of complex systems sciences. On the one hand, we use complex networks theory for the analysis of cell interactomes of the causative agent of the disease: the bacillus Mycobacterium tuberculosis. Here, we analyze the gene regulatory network of the bacterium, as well as its network of protein interactions and the way in which it is transformed as a consequence of gene expression adaptation to disparate environments. On the other hand, at the level of human societies, we develop new models for the description of TB spreading on complex populations. First, we develop mathematical models aimed at addressing, from a conceptual perspective, the interplay between complexity of hosts' populations and certain dynamical traits characteristic of TB spreading, like long latency periods and syndemic associations with other diseases. On the other hand, we develop a novel data-driven model for TB spreading with the objective of providing faithful impact evaluations for novel TB vaccines of different types

Statistical mechanics for biological applications: focusing on the immune system

The emergence in the last decades of a huge amount of data in many fields of biology triggered also an increase of the interest by quantitative disciplines for life sciences. Mathematics, physics and informatics have been providing quantitative models and advanced statistical tools in order to help the understanding of many biological problems. Statistical mechanics is a field that particularly contributed to quantitative biology because of its intrinsic predisposition in dealing with systems of many strongly interacting agents, noise, information processing and statistical inference. In this Thesis a collection of works at the interphase between statistical mechanics and biology is presented. In particular they are related to biological problems that can be mainly reconducted to the biology of the immune system. Beyond the unification key given by statistical mechanics of discrete systems and quantitative modeling and analysis of the immune system, the works presented here are quite diversified. The origin of this heterogeneity resides in the intent of using and learning many different techniques during the lapse of time needed for the preparation of the work reviewed in this Thesis. In fact the work presented in Chapter 3 mainly deals with statistical mechanics, networks theory and networks numerical simulations and analysis; Chapter 4 presents a mathematical physics oriented work; Chapter 5 and 6 deal with data analysis and in particular wth clinical data and amino acid sequences data sets, requiring the use of both analytical and numerical techniques. The Thesis is conceptually organized in two main parts. The first part (Chapters 1 and 2) is dedicated to the review of known results both in statistical mechanics and biology, while in the second part (Chapters 3, 4 and 6) the original works are presented together with briefs insights into the research fields in which they can be embedded. In particular, in Chapter 1 some of the most relevant models and techniques in statistical mechanics of mean field spin systems are reviewed, starting with the Ising model and then passing to the Sherrington-Kirkpatrik model for spin glasses and to the Hopfield model for attractors neural networks. The replica method is presented together with the stochastic stability method as a mathematically rigorous alternative to replicas. Chapter 2 is dedicated to a very schematic overview of the biology of the immune system. In Chapter 3, Section 3.1 is dedicated to the presentation of a mathematical phenomenological model for the study of the idiotypic network while Section 3.2 serves as a review of the statistical mechanics based models proposed by Elena 1 2 Introduction Agliari and Adriano Barra as toy models meant to underline the possible role of complex networks within the immune system. In Chapter 4 the mathematical model of an analogue neural network on a diluted graph is studied. It is shown how the problem can be mapped in a bipartite diluted spin glass. The model is rigorously solved at the replica symmetric level with the use of the stochastic stability technique and fluctuations analysis is used to study the spin glass transition of the system. A topological analysis of the network is also performed and different topological regimes are proven to emerge though the tuning of the model parameters. In Chapter 5 a model for the analysis of clinical records of testing sets of patients is presented. The model is based on a Markov chain over the space of clinical states. The machinery is applied to data concerning the insurgence of Tuberculosis and Non-Tuberculous Infections as side effects in patients treated with Tumor Necrosis Factor inhibitors. The analysis procedure is capable of capturing clinical details of the behaviors of different drugs. Lastly, Chapter 6 is dedicated to a statistical inference analysis on deep sequencing data of an antibodies repertoire with the purpose of studying the problem of antibodies affinity maturation. A partial antibodies repertoire from a HIV-1 infected donor presenting broadly neutralizing serum is used to infer a probability distribution in the space of sequences that is compared with neutralization power measurements and with the deposited crystallographic structure of a deeply matured antibody. The work is still in progress, but preliminary results are encouraging and are presented here

Statistical mechanics for biological applications: focusing on the immune system

The emergence in the last decades of a huge amount of data in many fields of biology triggered also an increase of the interest by quantitative disciplines for life sciences. Mathematics, physics and informatics have been providing quantitative models and advanced statistical tools in order to help the understanding of many biological problems. Statistical mechanics is a field that particularly contributed to quantitative biology because of its intrinsic predisposition in dealing with systems of many strongly interacting agents, noise, information processing and statistical inference. In this Thesis a collection of works at the interphase between statistical mechanics and biology is presented. In particular they are related to biological problems that can be mainly reconducted to the biology of the immune system. Beyond the unification key given by statistical mechanics of discrete systems and quantitative modeling and analysis of the immune system, the works presented here are quite diversified. The origin of this heterogeneity resides in the intent of using and learning many different techniques during the lapse of time needed for the preparation of the work reviewed in this Thesis. In fact the work presented in Chapter 3 mainly deals with statistical mechanics, networks theory and networks numerical simulations and analysis; Chapter 4 presents a mathematical physics oriented work; Chapter 5 and 6 deal with data analysis and in particular wth clinical data and amino acid sequences data sets, requiring the use of both analytical and numerical techniques. The Thesis is conceptually organized in two main parts. The first part (Chapters 1 and 2) is dedicated to the review of known results both in statistical mechanics and biology, while in the second part (Chapters 3, 4 and 6) the original works are presented together with briefs insights into the research fields in which they can be embedded. In particular, in Chapter 1 some of the most relevant models and techniques in statistical mechanics of mean field spin systems are reviewed, starting with the Ising model and then passing to the Sherrington-Kirkpatrik model for spin glasses and to the Hopfield model for attractors neural networks. The replica method is presented together with the stochastic stability method as a mathematically rigorous alternative to replicas. Chapter 2 is dedicated to a very schematic overview of the biology of the immune system. In Chapter 3, Section 3.1 is dedicated to the presentation of a mathematical phenomenological model for the study of the idiotypic network while Section 3.2 serves as a review of the statistical mechanics based models proposed by Elena 1 2 Introduction Agliari and Adriano Barra as toy models meant to underline the possible role of complex networks within the immune system. In Chapter 4 the mathematical model of an analogue neural network on a diluted graph is studied. It is shown how the problem can be mapped in a bipartite diluted spin glass. The model is rigorously solved at the replica symmetric level with the use of the stochastic stability technique and fluctuations analysis is used to study the spin glass transition of the system. A topological analysis of the network is also performed and different topological regimes are proven to emerge though the tuning of the model parameters. In Chapter 5 a model for the analysis of clinical records of testing sets of patients is presented. The model is based on a Markov chain over the space of clinical states. The machinery is applied to data concerning the insurgence of Tuberculosis and Non-Tuberculous Infections as side effects in patients treated with Tumor Necrosis Factor inhibitors. The analysis procedure is capable of capturing clinical details of the behaviors of different drugs. Lastly, Chapter 6 is dedicated to a statistical inference analysis on deep sequencing data of an antibodies repertoire with the purpose of studying the problem of antibodies affinity maturation. A partial antibodies repertoire from a HIV-1 infected donor presenting broadly neutralizing serum is used to infer a probability distribution in the space of sequences that is compared with neutralization power measurements and with the deposited crystallographic structure of a deeply matured antibody. The work is still in progress, but preliminary results are encouraging and are presented here

Biological Networks

Networks of coordinated interactions among biological entities govern a myriad of biological functions that span a wide range of both length and time scales—from ecosystems to individual cells and from years to milliseconds. For these networks, the concept “the whole is greater than the sum of its parts” applies as a norm rather than an exception. Meanwhile, continued advances in molecular biology and high-throughput technology have enabled a broad and systematic interrogation of whole-cell networks, allowing the investigation of biological processes and functions at unprecedented breadth and resolution—even down to the single-cell level. The explosion of biological data, especially molecular-level intracellular data, necessitates new paradigms for unraveling the complexity of biological networks and for understanding how biological functions emerge from such networks. These paradigms introduce new challenges related to the analysis of networks in which quantitative approaches such as machine learning and mathematical modeling play an indispensable role. The Special Issue on “Biological Networks” showcases advances in the development and application of in silico network modeling and analysis of biological systems

Multiscale Modeling of T Cells in Mycobacterium Tuberculosis Infection

Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is one of the deadliest infectious diseases in the world and remains a significant global health burden. Central to the immune response against Mtb are T cells, a type of adaptive immune cell that can kill infected cells, secrete cytokines to activate other immune cells, and orchestrate the broader immune response. Over the past few decades, experimental studies have significantly furthered our understanding of T-cell biology and function during Mtb infection. However, these findings have yet to translate to a clinically effective TB vaccine. As a complementary approach to experimental studies, systems biology and computational modeling can provide context to T-cell function by describing T-cell interactions with other immune cells across multiple scales. In this thesis we utilize a systems biology approach to characterize T-cell behavior, function, and movement across multiple physiological and temporal scales during Mtb infection. In addition, we develop a whole-host model of the immune response to Mtb. Following infection with Mtb, the immune response leads to the development of multiple lung granulomas – organized structures composed of immune cells that surround bacteria. Using a previously developed agent-based model of granuloma formation and function, we explore the role of T cells within the granuloma and predict that T-cell exhaustion, a type of T-cell dysfunction, is prevented from occurring by the physical structure of the granuloma. Next, we develop a novel whole lung model that tracks the formation of multiple granulomas. Using this model, we predict that a special type of T-cell, called a multi-functional CD8+ T cell, is key in preventing dissemination events - when bacteria escape one granuloma and seed the formation of a new one elsewhere in the lung. We also present a model of T-cell priming, proliferation, and differentiation within the lymph nodes and blood following TB vaccination and illustrate that non-human primates and humans respond similarly when receiving TB vaccination. We mathematically link the whole lung model and lymph node and blood model to create a whole-host model of the immune response following Mtb infection. We show that this model can capture various aspects of human and non-human primate TB disease and predict that biomarkers in the blood may only faithfully represent events in the lung at early time points after infection. Using this model, we predict that resident memory T cells are important mediators of protection against reinfection with Mtb and additionally predict the lifespan of these crucial cells in humans. Finally, we develop a protocol for calibrating mathematical and computational models to experimental datasets. Overall, this dissertation builds on our knowledge of the various roles T cells play in responding to Mtb infection, presents a set of computational models for evaluating the T-cell response to either infection or vaccination, and identifies mechanisms that control different outcomes across multiple scales following Mtb infection, reinfection, or vaccination.PHDBioinformaticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/167940/1/louisjos_1.pd

Modelling the genomic structure, and antiviral susceptibility of Human Cytomegalovirus

Human Cytomegalovirus (HCMV) is found ubiquitously in humans worldwide, and once acquired, the infection persists within the host throughout their life. Although Immunocompetent people rarely are affected by HCMV infections, their related diseases pose a major health problem worldwide for those with compromised or suppressed immune systems such as transplant recipients. Additionally, congenital transmission of HCMV is the most common infectious cause of birth defects globally and is associated with a substantial economic burden. This thesis explores the application of statistical modelling and genomics to unpick three key areas of interest in HCMV research. First, a comparative genomics analysis of global HCMV strains was undertaken to delineate the molecular population structure of this highly variable virus. By including in-house sequenced viruses of African origin and by developing a statistical framework to deconvolute highly variable regions of the genome, novel and important insights into the co-evolution of HCMV with its host were uncovered. Second, a rich database relating mutations to drug sensitivity was curated for all the antiviral treated herpesviruses. This structured information along with the development of a mutation annotation pipeline, allowed the further development of statistical models that predict the phenotype of a virus from its sequence. The predictive power of these models was validated for HSV1 by using external unseen mutation data provided in collaboration with the UK Health Security Agency. Finally, a nonlinear mixed effects model, expanded to account for Ganciclovir pharmacokinetics and pharmacodynamics, was developed by making use of rich temporal HCMV viral load data. This model allowed the estimation of the impact of immune-clearance versus antiviral inhibition in controlling HCMV lytic replication in already established infections post-haematopoietic stem cell transplant

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

Multiscale Modeling of Tuberculosis Disease and Treatment to Optimize Antibiotic Regimens

Tuberculosis (TB) is one of the world’s deadliest infectious diseases. Caused by the pathogen Mycobacterium tuberculosis (Mtb), the standard regimen for treating TB consists of treatment with multiple antibiotics for at least six months. There are a number of complicating factors that contribute to the need for this long treatment duration and increase the risk of treatment failure. Person-to-person variability in antibiotic absorption and metabolism leads to varying levels of antibiotic plasma concentrations, and consequently lower concentrations at the site of infection. The structure of granulomas, lesions forming in lungs in response to Mtb infection, creates heterogeneous antibiotic distributions that limit antibiotic exposure to Mtb. Microenvironments in the granuloma can shift Mtb to phenotypic states that have higher tolerances to antibiotics. We can use computational modeling to represent and predict how each of these factors impacts antibiotic regimen efficacy and granuloma sterilization. In this thesis, we utilize an agent-based, computational model called GranSim that simulates granuloma formation, function and treatment. We present a method of incorporating sources of heterogeneity and variability in antibiotic pharmacokinetics to simulate treatment. Using GranSim to simulate treatment while accounting for these sources of heterogeneity and variability, we discover that individuals that naturally have low plasma antibiotic concentrations and granulomas with high bacterial burden are at greater risk of failing to sterilize granulomas during antibiotic treatment. Importantly, we find that changes to regimens provide greater improvements in granuloma sterilization times for these individuals. We also present a new pharmacodynamic model that incorporates the synergistic and antagonistic interactions associated with combinations of antibiotics. Using this model, we show that in vivo antibiotic concentrations impact the strength of these interactions, and that accounting for the actual concentrations within granulomas provides greater predictive power to determine the efficacy of a given antibiotic combination. A goal in improving antibiotic treatment for TB is to find regimens that can shorten the time it takes to sterilize granulomas while minimizing the amount of antibiotic required. With the number of potential combinations of antibiotics and dosages, it is prohibitively expensive to exhaustively simulate all combinations to achieve these goals. We present a method of utilizing a surrogate-assisted optimization framework to search for optimal regimens using GranSim and show that this framework is accurate and efficient. Comparing optimal regimens at the granuloma scale shows that there are alternative regimens using the antibiotic combination of isoniazid, rifampin, ethambutol and pyrazinamide that could improve sterilization times for some granulomas in TB treatment. In virtual clinical trials, these alternative regimens do not outperform the regimen of standard doses but could be acceptable alternatives. Focusing on identifying alternative regimens that can improve treatment for high risk patients could help to significantly decrease the global burden for TB. Overall, this thesis presents a computational tool to evaluate antibiotic regimen efficacy while accounting for the complicating factors in TB treatment and improves our ability to predict new regimens that can improve clinical treatment of TB.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166103/1/cicchese_1.pd