Modeling, analysis, and control of Syk-mediated signaling events for B cells and associated cellular response for B cells
Understanding the immune system and its responses to foreign threats (antigens) is a matter of understanding the immune cells involved, their individual responses, and chemicals responsible for intracellular and intercellular communication. The overall immune response is driven by individual actions of neutrophils, antigen-presenting cells, and lymphocytes (T cells and B cells), among other cells. Intercellular communication is the means by which immune cells develop coordinated response while intracellular signals determine responses within a cell; both depend on systems of chemical reactions at their respective scales. The perspective taken in this dissertation is that of understanding B cells at the intracellular scale and the signaling molecules responsible for its responses. B cells, a type of white blood cell in the immune system, identify antigens by binding to them via B cell receptors (BCRs). After identifying an antigen, mechanisms in the B cell membrane initiate a system of chemical interactions that propagate an intracellular signal and thereby determining the cell's response. In the first part of this thesis, we present a model for B cell signaling using dynamical systems and motivated by the desire to understand the role of the protein Syk. Syk is intricately involved in the early signaling events and is required for proper response to an antigen. The importance of this protein has led to mutant variants being genetically engineered to manipulate its impact. This mutant variant is one of the primary novelties of our model, and allows us to investigate the role of feedback loops involving Syk in producing responses. This mutant model is used to develop testable hypothesis regarding the B cell mutant kinase known as Syk-AQL. It is often difficult to resolve questions regarding complicated biological systems through experimentation alone; this has led to the rise in the use of mathematical modeling in systems biology. Experimentation is still important, however, as data is needed to refine models, and designing experiments to most efficiently refine models is an important topic of research. This is a motivation for an interest in model-based experimental design, where experiments can be systematically chosen to reduce dynamic uncertainty in a given model. In the second part of this thesis, we provide background on methods of experiment design and discuss the Maximal Informative Next Experiment (MINE) method in greater detail. In particular, we provide a theoretical foundation for this method and prove a convergence result for MINE with nonlinear models. Design criteria have been developed to sequentially provide maximal reduction in uncertainty and one criterion has been rigorously justified. We will extend this analysis to other design criteria and in more general contexts. Experimental design results will be useful for work on B cell modeling as well as other applications. This project is a step towards better understanding cellular response and creating tools useful modeling biological systems.
Buzzard, Purdue University.
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