Rice University Research Repository
The Rice Research Repository (R-3) provides access to research produced at Rice University, including theses and dissertations, journal articles, research center publications, datasets, and academic journals. Managed by Fondren Library, R-3 is indexed by Google and Google Scholar, follows best practices for preservation, and provides DOIs to facilitate citation. Woodson Research Center collections, including Rice Images and Documents and the Task Force on Slavery, Segregation, and Racial Injustice, have moved here.
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Engineering synthetic phosphorylation signaling networks in human cells
(2025-01-15) Yang, Xiaoyu; Bashor, Caleb; Mehta, Pankaj
Protein phosphorylation signaling networks have a central role in how cells sense and respond to their environment. This thesis outlines a comprehensive approach to designing and implementing synthetic phosphorylation networks in mammalian cells, using modular protein domain parts to construct reversible phosphorylation cycles and assemble customizable circuits. By leveraging model-guided tuning, these engineered circuits enable precise signal processing and facilitate the creation of diverse network connections. The synthetic pathways can be linked to upstream cell surface receptors for rapid sensing of extracellular ligands and downstream elements that regulate gene expression.
The work further explores the application of these synthetic networks in therapeutically relevant settings. We demonstrate how engineered circuits can detect physiologically significant biomolecules, such as inflammation markers, and respond with targeted actions, including the controlled secretion of therapeutic proteins. The successful integration and functional testing of these synthetic pathways in primary human cells highlight a significant step toward their use in cell-based therapies. This adaptability illustrates the potential for engineering customized cellular responses tailored to specific disease states, paving the way for innovative treatment strategies.
By providing a robust toolkit and showcasing its versatility, this thesis lays the groundwork for future advancements in synthetic biology. The modular design and adaptability of these synthetic signaling networks create opportunities for developing programmable cellular systems capable of addressing a wide range of biotechnological and medical challenges. This work contributes to the growing field of synthetic biology by establishing a foundational framework for integrating engineered pathways into cellular systems, enhancing their ability to perform complex, tailored functions and expanding the scope of potential applications in biosensing and therapeutic development.
Nonlocal and nonlinear optical response and STM studies of quantum materials
(2025-04-22) Zhang, Ding; Yi, Ming; Naik, Gururaj
Quantum materials are unique in their long-range interactions and competing phases tunable by external stimuli. Due to the incommensuracy of the quantum order or competing phases, the volume of quantum materials is partitioned into multiple domains. Light-matter interaction in quantum materials presents a new paradigm as light can tip the balance between many competing quantum many-body phases and give rise to new phenomena.
In the field of light probing quantum materials, most studies focus on ultrashort high-energy probing; rarely has anyone tried to use low-energy light to probe the material in the linear response regime and still get interesting results. In this dissertation, I will present the results of low-intensity light probing of quantum materials. Firstly, I present a nonlocal model of the dielectric function and show it can accurately describe the angle-resolved spectrum of TaS2 in the visible. The competing stacking configurations of the charge domains in this layered material result in significant optical inhomogeneity that necessitates a nonlocal dielectric function. I performed intensity-sweep characterizations and used our model to predict the domain size dependence on light intensity. The non-local parameter extracted from our measurements sheds light on the competition between the two stacking orders.
Next, seeking direct microscopic evidence of light-induced stacking reconfigurations, I present our experimental results from the Laser-STM system probing the surface charge density under stable laser illumination. Despite the noise at room temperature and laser power instability, which prevent an accurate determination of stacking order configurations, the TaS2 topography images uniquely exhibit a clear low-frequency charge-density oscillation on the order of 0.2 Hz. To investigate the dynamics of this light-matter interaction, an optical chopper is used to modulate the laser illumination. I demonstrate the emergence of a breathing charge density wave modulated by the chopping frequency. Furthermore, I propose our conjectures and hypotheses regarding the physics underlying this novel phenomenon. Finally, I will present simulations results that utilize quantum materials to realize advanced phase control and to design an anomalous diffraction grating.
Parameter Estimation of Neuron Models using Subset Selection and Dynamic Optimization
(2025-04-23) Khaddaj, Anwar; Heinkenschloss, Matthias
This thesis presents two frameworks for parameter estimation in neuron models and assesses parameter accuracy by constructing confidence regions of parameter estimates.
Parameter estimation helps advance the understanding of how neurons process sensory information. Nonlinear least squares have previously been used to fit biophysical neuron models. Yet, little attention has been devoted to handling rank-deficient problems, and to identifying and characterizing possible degeneracy in model parameters. To identify parameter degeneracy and resolve the rank deficiency, an SVD-based subset selection algorithm is used. Additional biophysical experiments are constructed, which constrain the least identifiable parameters. The approach is applied to the HCN neuron model. Moreover, an all-at-once optimization approach is applied, which includes the neuron model as a constraint and views parameters and model solution as optimization variables. This approach is demonstrated on the Pinsky-Rinzel model. The framework is designed to support the goal of applying them to complex compartmental neuron models.
Overcoming Mesoscale Recording Challenges with Scalable Microfabrication Techniques
(2025-04-23) Pathirana, Priyan V; Seymour, John P
Implantable microelectrode arrays have revolutionized neuroscience, promising future breakthroughs in translational research. However, in-vivo applications are constrained due to the mechanical mismatch between neural tissue and the electrode interface, as the relatively high stiffness of the implant triggers an immune response and neuron loss. Traditional substrate material choices (such as silicon or polyimide) sustain only minor strains before failure, further limiting implant lifetime. The relationship between material properties, such as shore hardness and water absorption, and their impact on the performance of patterned structures requires further investigation for various silicones and other semi-flexible substrates. In our work, we explore the potential of silicone-based substrates to bridge this mechanical gap between neural tissue and substrates by examining different mechanical properties to pinpoint the critical factors for achieving fine metal patterning. For this study, we juxtapose NUSIL MED-6019, a significantly higher durometer silicone compared to the gold standard of Sylgard 184. We hypothesize that systematically controlling the material properties of silicones will enable high-density patterning that can mitigate the thermal stresses induced during photolithography and other thermal processes. These experiments facilitate the creation of diagnostic and therapeutic bioelectronics with greater mechanical and electrical robustness.
Development of Nanomechanical Testing Methods and Nanomechanical Behavior of Materials Under Extreme Conditions
(2025-04-03) Steinbach, Douglas; Lou, Jun
Nano and micromechanical experiments allow for a fundamental
understanding of how small-scale materials behave and, thus, what applications
these materials can be practically applied to. For example, graphene and hexagonal
boron nitride (hBN) are structurally similar. However, graphene is stronger, but
hBN is tougher. This fundamental insight enables engineers to design composites
balancing strength versus toughness. The more nanomaterials studied and the
greater the number of methods used to study nanomechanics, the more
nanomaterials are applied, likely as composites. Due to the unique properties of
nanomaterials, they are strong candidates to reinforce composites in extreme
applications such as high radiation environments. However, testing methods are
lacking at the nano- and microscale, as manufacturing new setups is complicated
and niche, and some existing testing methods have yet to be applied to
nanocomposites.
Many mechanical properties have yet to be directly measured at the
nanoscale (e.g. high strain rate tension and out-of-plane shear of a 2D material).
Nanoscale high-strain rate methods have been devised for 1D materials, but not all
methods are applicable to 2D materials. In this thesis, a high strain rate tensile
method is developed and validated on 1D PMMA. With this proof of concept,
multilayer hBN was subjected to high strain rate tension. This validated the
applicability of using a push-to-pull microdevice with a 2D material at high strain
rates. This method found a strain rate sensitivity in PMMA but not in hBN. Designs
for in- and out-of-plane shear devices are explored as well. Two separate material
platforms were investigated to understand how 1D and 2D reinforcing
nanomaterials affect the mechanical properties of composites with radiation
exposure at the microscale. Those composites are carbon nanotube-reinforced
silicon carbide (SiC) exposed to radiation and an hBN-reinforced covalent organic
framework (COF) exposed to radiation. Pillar splitting, a simple microscale fracture
toughness test, has yet to be performed on a nanomaterial-reinforced composite.
Pillar splitting SiC showed the method's limitations when samples are highly
defective. Nanotube reinforcement led to a weaker material likely correlated to
processing more than reinforcement, while radiation increased the toughness. The
hBN/COF composite showed that neutron radiation can alter the detectable bonds
in the composite and strengthen, harden, and toughen the composite.