Multi-Scale Modeling of the Innate Immune System: A Dynamic Investigation into Pathogenic Detection

Having a well-functioning immune system can mean the difference between a mild ailment and a life-threatening infection; however, predicting how a disease will progress has proven to be a significant challenge. The dynamics driving the immune system are governed by a complex web of cell types, signaling proteins, and regulatory genes that have to strike a balance between disease elimination and rampant inflammation. An insufficient immune response will induce a prolonged disease state, but an excessive response will cause unnecessary cell dead and extensive tissue damage. This balance is usually self-regulated, but medical intervention is often necessary to correct imbalances. Unfortunately, these therapies are imperfect and accompanied by mild to debilitating side-effects caused by off-target effects. By developing a detailed understanding of the immune response, the goal of this dissertation is to predict how the immune system will respond to infection and determine how new potential therapies could overcome these threats. Computational modeling provides an opportunity to synthesize current immunological observations and predict response outcomes to pathogenic infections. When coupled with experimental data, these models can simulate signaling pathway dynamics that drive the immune response, incorporate regulatory feedback mechanisms, and model inherent biological noise. Taken together, computational modeling can explain emergent behavior that cannot be determined from experiment alone. This dissertation will unitize two computational modeling techniques: ordinary differential equations (ODEs) and agent-based modeling (ABMs). Ultimately, they are combined in a novel way to model cellular immune responses across multiple length scales, creating a more accurate representation of the pathogenic response. TLR4 and cGAS signaling are prominent in a number of diseases and dysregulations including---but not limited to---autoimmunity, cancer, HIV, HSV, tuberculosis, and sepsis. These two signaling pathways are so prevalent because they are activated extremely early and help drive the downstream immune signaling. Modeling how cells dynamically regulate these pathways is critical for understanding how diseases circumvent feedback mechanisms and how new therapies can restore immune function to combat disease progression. By using ODE and ABM techniques, these studies aim to incrementally expand our knowledge of innate immune signaling and understand how feedback mechanisms control disease severity

A unified inter-host and in-host model of antibiotic resistance and infection spread in a hospital ward

As the battle continues against hospital-acquired infections and the concurrent rise in antibiotic resistance among many of the major causative pathogens, there is a dire need to conduct controlled experiments, in order to compare proposed control strategies. However, cost, time, and ethical considerations make this evaluation strategy either impractical or impossible to implement with living patients. This paper presents a multi-scale model that offers promise as the basis for a tool to simulate these (and other) controlled experiments. This is a “unified” model in two important ways: (i) It combines inter-host and in-host dynamics into a single model, and (ii) it links two very different modeling approaches - agent-based modeling and differential equations - into a single model. The potential of this model as an instrument to combat antibiotic resistance in hospitals is demonstrated with numerical examples

Data-driven modelling of biological multi-scale processes

Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers