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The advent of new high throughput technology allows for increasingly detailed characterization of the immune system in healthy, disease, and age states. The immune system is composed of two main branches: the innate and adaptive immune system, though the border between these two states is appearing less distinct. The adaptive

The advent of new high throughput technology allows for increasingly detailed characterization of the immune system in healthy, disease, and age states. The immune system is composed of two main branches: the innate and adaptive immune system, though the border between these two states is appearing less distinct. The adaptive immune system is further split into two main categories: humoral and cellular immunity. The humoral immune response produces antibodies against specific targets, and these antibodies can be used to learn about disease and normal states. In this document, I use antibodies to characterize the immune system in two ways: 1. I determine the Antibody Status (AbStat) from the data collected from applying sera to an array of non-natural sequence peptides, and demonstrate that this AbStat measure can distinguish between disease, normal, and aged samples as well as produce a single AbStat number for each sample; 2. I search for antigens for use in a cancer vaccine, and this search results in several candidates as well as a new hypothesis. Antibodies provide us with a powerful tool for characterizing the immune system, and this natural tool combined with emerging technologies allows us to learn more about healthy and disease states.
ContributorsWhittemore, Kurt (Author) / Sykes, Kathryn (Thesis advisor) / Johnston, Stephen A. (Committee member) / Jacobs, Bertram (Committee member) / Stafford, Phillip (Committee member) / Stout, Valerie (Committee member) / Arizona State University (Publisher)
Created2014
Description

We implemented the well-known Ising model in one dimension as a computer program and simulated its behavior with four algorithms: (i) the seminal Metropolis algorithm; (ii) the microcanonical algorithm described by Creutz in 1983; (iii) a variation on Creutz’s time-reversible algorithm allowing for bonds between spins to change dynamically; and

We implemented the well-known Ising model in one dimension as a computer program and simulated its behavior with four algorithms: (i) the seminal Metropolis algorithm; (ii) the microcanonical algorithm described by Creutz in 1983; (iii) a variation on Creutz’s time-reversible algorithm allowing for bonds between spins to change dynamically; and (iv) a combination of the latter two algorithms in a manner reflecting the different timescales on which these two processes occur (“freezing” the bonds in place for part of the simulation). All variations on Creutz’s algorithm were symmetrical in time, and thus reversible. The first three algorithms all favored low-energy states of the spin lattice and generated the Boltzmann energy distribution after reaching thermal equilibrium, as expected, while the last algorithm broke from the Boltzmann distribution while the bonds were “frozen.” The interpretation of this result as a net increase to the system’s total entropy is consistent with the second law of thermodynamics, which leads to the relationship between maximum entropy and the Boltzmann distribution.

ContributorsLewis, Aiden (Author) / Chamberlin, Ralph (Thesis director) / Beckstein, Oliver (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Department of Physics (Contributor)
Created2023-05