Multi-Resolution Sensitivity Analysis of Model of Immune Response to Helicobacter pylori Infection via Spatio-Temporal Metamodeling

Computational immunology studies the interactions between the components of the immune system that includes the interplay between regulatory and inflammatory elements. It provides a solid framework that aids the conversion of pre-clinical and clinical data into mathematical equations to enable modeling and in silico experimentation. The modeling-driven insights shed lights on some of the most pressing immunological questions and aid the design of fruitful validation experiments. A typical system of equations, mapping the interaction among various immunological entities and a pathogen, consists of a high-dimensional input parameter space that could drive the stochastic system outputs in unpredictable directions. In this paper, we perform spatio-temporal metamodel-based sensitivity analysis of immune response to Helicobacter pylori infection using the computational model developed by the ENteric Immune SImulator (ENISI). We propose a two-stage metamodel-based procedure to obtain the estimates of the Sobol' total and first-order indices for each input parameter, for quantifying their time-varying impacts on each output of interest. In particular, we fully reuse and exploit information from an existing simulated dataset, develop a novel sampling design for constructing the two-stage metamodels, and perform metamodel-based sensitivity analysis. The proposed procedure is scalable, easily interpretable, and adaptable to any multi-input multi-output complex systems of equations with a high-dimensional input parameter space

Agent-based modelling of the spatio-temporal interaction between immune cells and human-pathogenic fungi

Human-pathogene Pilze stellen aufgrund der zunehmenden Anzahl von immungeschwächten Patienten ein zunehmendes Problem im Gesundheitswesen dar und sind mit hohen Sterblichkeitsraten assoziiert. Das menschliche Immunsystem ein hochkomplexes System stellt ein Arsenal an Effektormechanismen bereit, die den gesunden Zustand des Wirts schützen. Vielfältige Ursachen können jedoch diese schützende Funktion des Immunsystems beeinträchtigen, was es eindringenden Mikroben erlaubt, schwere Infektionen zu verursachen. Die Forschung an Wirt-Pathogen-Interaktionen zwischen humanpathogenen Pilzen und dem Immunsystem ist essentiell für die Entwicklung neuer diagnostischer und therapeutischer Verfahren. In dieser Arbeit wurden diese Wirt-Pathogen-Interaktionen entsprechend des Konzepts der Systembiologie, untersucht. Basierend auf experimentellen Daten wurden virtuelle Infektionsmodelle entwickelt, um die treibenden Kräfte der angeborenen Immunantwort gegen die pilzlichen Erreger Candida albicans, Candida glabrata und Aspergillus fumigatus zu entschlüsseln. ..

A Markov decision process embedded with predictive modeling: a modeling approach from system dynamics mathematical models, agent-based models to a clinical decision making

Doctor of PhilosophyDepartment of Industrial & Manufacturing Systems EngineeringDavid H. Ben-AriehChih-Hang WuPatients who suffer from sepsis or septic shock are of great concern in the healthcare system. Recent data indicate that more than 900,000 severe sepsis or septic shock cases developed in the United States with mortality rates between 20% and 80%. In the United States alone, almost $17 billion is spent each year for the treatment of patients with sepsis. Clinical trials of treatments for sepsis have been extensively studied in the last 30 years, but there is no general agreement of the effectiveness of the proposed treatments for sepsis. Therefore, it is necessary to find accurate and effective tools that can help physicians predict the progression of disease in a patient-specific way, and then provide physicians recommendation on the treatment of sepsis to lower risk for patients dying from sepsis. The goal of this research is to develop a risk assessment tool and a risk management tool for sepsis. In order to achieve this goal, two system dynamic mathematical models (SDMMs) are initially developed to predict dynamic patterns of sepsis progression in innate immunity and adaptive immunity. The two SDMMs are able to identify key indicators and key processes of inflammatory responses to an infection, and a sepsis progression. Second, an integrated-mathematical-multi-agent-based model (IMMABM) is developed to capture the stochastic nature embedded in the development of inflammatory responses to a sepsis. Unlike existing agent-based models, this agent-based model is enhanced by incorporating developed SDMMs and extensive experimental data. With the risk assessment tools, a Markov decision process (MDP) is proposed, as a risk management tool, to apply to clinical decision-makings on sepsis. With extensive computational studies, the major contributions of this research are to firstly develop risk assessment tools to identify the risk of sepsis development during the immune system responding to an infection, and secondly propose a decision-making framework to manage the risk of infected individuals dying from sepsis. The methodology and modeling framework used in this dissertation can be expanded to other disease situations and treatment applications, and have a broad impact to the research area related to computational modeling, biology, medical decision-making, and industrial engineering