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Introduction

Texas ScholarWorks was established to provide open, online access to the products of the University's research and scholarship, to preserve these works for future generations, to promote new models of scholarly communication, and to help deepen community understanding of the value of higher education.

UT Tower and campus image credit: Earl McGehee, CC-BY, https://www.flickr.com/photos/ejmc/7452145850

 

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Recent Submissions

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Disease detection and lipid membrane analysis using spectrometric and spectroscopic measurements
(2023-05) Povilaitis, Sydney Cordano; Webb, Lauren J.; Baiz, Carlos R; Brenna, Thomas J; Eberlin, Livia S; Elber, Ron
The biological membrane functions as a semipermeable barrier regulating molecular flux from the cell and is a complex system with a diverse composition of lipids. Lipids are a varied class of biomolecules ranging from sterols to phospholipids. Both small changes in structure and relative amount of different lipids will affect membrane properties and processes. Importantly, phospholipid homeostasis is disrupted in many disease states, resulting in different membrane lipid compositions which is useful as a diagnostic tool and could be leveraged for drug-delivery. This dissertation explores the diversity and function of membrane lipids from two perspectives: as biomarkers for disease detection and as modulators of membrane-small molecule interactions and electric fields. The first section describes the use of a mass spectrometry-based sampling probe, the MasSpec Pen, for rapid analysis of lipids and metabolites. This probe was applied to acquire molecular profiles from bacteria and infected biospecimens which were then used to build statistical models for bacterial classification. The identification of a putatively assigned anesthetic metabolite, hexafluorisopropyl sulfonate, using this system is also described. Lastly, we report the development of an interface to facilitate clinical implementation of the MasSpec Pen and a proof-of-concept study distinguishing healthy liver tissue from hepatocellular carcinoma. The second section examines the implications of membrane lipid diversity for drug delivery and its impact on membrane electrostatics using fluorescence solvatochromism and vibrational stark effect (VSE) spectroscopy of model membrane systems. With complementary experiments and simulations, we propose charge separation on doubly positively charged peptides as a design principle for membrane permeating peptides. We also investigate the leaflet-dependent effects of anionic lipids on peptide-membrane interactions and identify outer leaflet anionic lipids as the dominant promoters of cationic peptide membrane insertion. The final chapter in this section probes the effect of cholesterol and 7-dehydrocholesterol (7DHC) on membrane electrostatics using VSE spectroscopy. While there was no significant difference in vibrational energy shifts for these two sterols, we demonstrate the significance of temperature for these measurements. Together, these efforts seek to expand the understanding of how biological membrane lipid diversity can be used to understand, diagnose, and treat disease states.
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The justification of religious intolerance : an examination of the American media's responses to the life and crimes of Nidal Hasan
(2023-08) Nnani, Donatus Haig; Seales, Chad E.
An examination of the crimes of Nidal Hasan through the lens of American News media. This document examines the production of religious extremism in relation to Islam for the consumption of the American public and how it is reductive and harmful to Muslims.
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Moving horizon optimization methods, applications and tools for learning and controlling dynamical systems
(2023-05) Lejarza, Fernando; Baldea, Michael; Hanasusanto, Grani A.; Edgar, Thomas F.; Stadtherr, Mark A.
Mathematical models based on dynamical systems are crucial for understanding complex phenomena across a wide range of scientific and engineering disciplines. Optimizing these models can significantly improve the performance (e.g., in the sense of socioeconomic, environmental, and safety concerns) of various processes and systems that support our modern society, such as e.g. supply chain networks and chemical manufacturing processes. However, controlling these systems in the presence of uncertainty and for high-dimensional models is challenging. Developing robust and efficient optimization models and solution algorithms for this purpose is therefore crucial. Similarly, optimization techniques can be used to infer the governing equations for such dynamical systems from available measurement data. Learning such models is important not only for performing the aforementioned control tasks, but also for advancing our understanding of the physical laws that govern the phenomena we have so long observed but cannot quantitatively explain. Motivated by the above, this dissertation contributes novel moving horizon optimization methods, applications and tools for learning and controlling a variety of dynamical systems. The first part of this dissertation introduces the background and theory of moving horizon estimation and control methods. As a motivating example, I present a novel application of these existing methods to the optimal data-driven management of the COVID-19 pandemic in the US. The proposed approach identifies optimal social distancing and testing policies that minimize socioeconomic impact, while keeping the the number of infected individuals under a specified threshold. Subsequently, I focus on dynamical system models corresponding networks of integrators for optimal supply chain management under uncertainty. The first methodological contribution corresponds to a tube-based robust economic model predictive control framework for sparse storage systems, which I shown to have improved feasibility for supply chain management under demand disturbances. The proposed approach significantly improves computational performance relative to the available methods. Subsequently, I develop an extensive and systematic case study evaluating the performance of deterministic (feedback-based, closed-loop, or online) moving horizon optimization in comparison to stochastic and robust methods for supply chain management under increasing levels of uncertainty, forecasting errors, and recourse availability. Having demonstrated the overall robust and computationally efficient performance of deterministic moving horizon optimization techniques, the second part of the dissertation is focused on a class of multi-scale dynamical systems corresponding to supply chains of highly perishable inventory. This type of supply chains require integration of the inventory management problem with quality control by manipulating environmental conditions (e.g., temperature) during shipment and storage, which directly impact the product deterioration rate. To this end, I introduce a novel modeling approach for incorporating complex, multivariate physico-chemical product quality dynamics within the supply chain inventory balances, and provide a computationally efficient reformulation thereof. Based on this modeling approach and the results introduced in Part I of the dissertation, I develop a stabilizing closed-loop optimal supply chain production and distribution planning framework to handle uncertainties, such as random customer demand and/or random product quality spoilage. I then propose a scalable solution heuristic approach to cope with larger supply chain networks, and I present several case studies to demonstrate robustness to demand uncertainty. Lastly, I develop a simultaneous state estimation and closed-loop control approach to account for the fact that product quality may not be completely measurable in practical settings. In the third and final part of the dissertation, the focus shifts from controlling dynamical systems to learning their governing equations from data via moving horizon optimization. Here, I develop methods based on dynamic nonlinear optimization which, compared to existing efforts, demonstrate greater flexibility for handling highly nonlinear systems, for incorporating prior domain knowledge, and coping with high amounts of measurement noise in the training data. I then demonstrate the extension of this learning framework to the case of reactive dynamical system and present numerical experiments for non-isothermal continuous and batch chemical reactors. Lastly, I develop a sequential dynamic nonlinear optimization approach for discovering and performing dimensionality reduction of microkinetic reaction networks.
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Layered syndrome based double error correcting codes for RRAM cells
(2023-05) Dutta, Shruti; Touba, Nur A.
Applications involving machine learning and neural networks have become increasingly essential in the AI revolution. Emerging trends in Resistive RAM memory technologies provide high-speed, low-cost, scalable solutions for such applications. These RRAM cells provide efficient and sophisticated memory hardware structures for machine-learning applications. However, it’s difficult to achieve reliable multilevel cell storage capacity in these memory technologies due to the occurrence of soft and hard errors. As these memories can store multi-bits per cell, it is important to explore limited magnitude symbols (multi-bit) error correction in RRAM. In this report, we propose a novel syndrome-based double error correcting code making efficient use of the inherent additive nature of RRAM cells for correcting errors in the symbol values. The proposed ECC divides the syndromes into groups and uses addition and XOR operations to correct double limited magnitude errors in the RRAM cells. It requires fewer checkbits, and since the memory itself can perform the addition through current summing, the decoder area and power are reduced.
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Accelerating the biotechnology revolution with machine learning-guided protein engineering
(2023-05) Diaz, Daniel Jesus; Ellington, Andrew D.; Anslyn, Eric V., 1960-; Henkelman, Graeme; Wilke, Claus; Klivans, Adam; Marcotte, Edward
An extremely important task in biotechnology is the ability to engineer proteins by introducing mutations into their sequences, which ultimately alters their folded structure and function. In nature, this process occurs via random mutation and selection, also known as evolution. Protein engineers have learned to limit the randomness and “direct” evolution, but this process is still too laborious and bottlenecks the application of biotechnology across all sectors of society. Machine learning (ML) guided protein engineering has the potential to revolutionize the development of protein-based biotechnology and enabling this future is the underlying theme of this thesis. To make meaningful advancements and enable ML-guided protein engineering both computational advancement and experimental validation are required. This dissertation presents studies that explore the capabilities of ML frameworks to protein data and experimental validation of structure-based ML frameworks. The first computational study examines the mutational landscape of proteins through the lens of 3D convolutional neural networks (3DCNNs) and evolution. The second study explores how to leverage recent advancements made in protein large language models (pLLMs) for supervised learning on protein stability. In this study, a supervised dataset that uses organism growth temperatures as coarse-grained label is curated and several machine learning techniques invented by the natural language and computer vision community are applied to fine-tune the pLLM, ESM-1b, to predict changes in thermal stability. On the experimental side, three studies on ML-guided protein engineering are presented. First, we used MutCompute, a 3D convolutional neural network (3DCNN), to identify stabilizing mutations on several PET hydrolase scaffolds and demonstrate the ML-engineered variants provide an avenue for the bioremediation of PET. Next, we demonstrate the utility of ML-guided protein engineering for the development of pandemic response biotechnology by stabilizing Bst DNA polymerase to enable low-resource COVID19 diagnostics. The third study is the capstone of this thesis. Here, a structure-based residual neural network (MutComputeX) is trained to generalize to protein-ligand interactions and a ML pipeline for the computational generation of protein-ligand complexes is developed and then combined to guide the active site engineering of norbelladine 4O-methyltransferase, a key enzyme for the biomanufacturing of the FDA-approved drug galantamine. This is the first demonstration of ML-guided active site engineering from a computational generated protein-ligand-cofactor ternary structure. Overall, these computational advancements and empirical validations of ML-guided protein engineering demonstrate that the future of industrial chemistry is a biological one.