The Effects Of Farnesyltransferase Inhibitor Abt-100 On Murine Helper T-Cell Differentiation And Cytokine Secretion

Farnesyltransferase Inhibitors (FTIs) are a class of drugs known to prevent the farnesylation and subsequent membrane attachment of a number of intracellular proteins. In various studies, the administration of FTIs has been found to play a role in the activation and development of T-cells in the immune system. FTIs have also been found to act as immunomodulators in delaying MHC-II mismatched skin allografts in mice. This study focuses on the effect of the FTI, ABT-100, on the differentiation and cytokine secretion of Th1 and Th2 helper T-cells in BALB/C mice to better understand which immune responses are targeted by FTIs. Splenocytes were isolated from BALB/C mice, skewed towards either a Th1 or a Th2 phenotype with the addition of cytokines, and treated with various concentrations of ABT-100. Splenocytes were also isolated and immediately cultured in the presence of ABT-100 to observe differentiation trends of helper T-cells. Cytokine production was measured using intracytoplasmic flow cytometry analysis. I found that ABT-100 treatment does not block Th1 or Th2 cell differentiation. Instead, ABT-100 treatment appears to affect cytokine production from effector T-cells. I found that ABT-100 causes a decrease in IFN-¿ production in mature Th1 cells yet does not affect IL-4 production in mature Th2 cells. This decrease in cytokine production as a result of ABT-100 treatments provides a potential mechanism for how ABT-100 works to delay MHC-II mismatched allograft rejection.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.