Caldicellulosiruptor is a unique genus of bacteria that produces lignocellulose-degrading Carbohydrate Active enZymes (CAZymes). CAZymes are of particular interest because of their potential utility in processing cellulose for sustainable biofuel pathways. However, studying the CAZyme efficiently has been difficult both experimentally and computationally due to the enzyme’s large size, over 1700 amino acid residues in length. Previous computational studies have studied the CAZyme in a limited capacity, simulating only small sections of the protein with atomistic approaches. In this paper, I present Mpipi- Cellulose, the extension of the coarse-grained Mpipi force field to cellulose. Mpipi-Cellulose represents each monomer of cellulose as a single interaction site (or bead), allowing for more efficient computations yet still accurately capturing the cellulose-protein interactions of CAZyme binding to cellulose. Through a series of umbrella sampling simulations, potentials of mean force were constructed for interactions between cellulose and a variety of ligands, including peptide fragments and subdomains of CelA, the primary CAZyme produced by the Caldicellulosiruptor genus of bacteria. Mpipi-Cellulose demonstrated strong qualitative agreement with Martini3, a higher resolution coarse-grained model optimized for cellulose, across these simulations. Mpipi-Cellulose was then used to simulate the entire CelA interacting with a slab of crystalline cellulose. Using the newly developed model, I tracked the number of contacts between the enzyme and the cellulose, showing that the majority of the residues that interact with the cellulose come from the binding domains as predicted by experiments. Further simulations were performed with various CelA mutants, removing different binding domains and exploring the impact on interaction with the cellulose. These simulations demonstrated that the second and third binding domains in CelA are particularly important for proper binding to the cellulose surface. With this knowledge and further use of the Mpipi-Cellulose model, a wide number of mutants can be screened to optimize CAZyme performance for degradation of cellulose to produce biofuels.

Microtubules (MTs) are key components of the cytoskeleton and play a critical role in cell growth and division. During mitosis, the mitotic spindle, composed of dynamic MTs, acts as a structural framework that captures and segregates chromosomes into daughter cells. Although MTs begin assembling the spindle during prophase, the precise mechanisms initiating this process remain unclear. It is known that RCC1, the most upstream regulator of the Ran pathway, generates RanGTP to release spindle assembly factors that promote MT formation. However, the spatial and quantitative requirements for RCC1 in spindle assembly are still poorly understood. Given RCC1’s essential role in initiating this process, determining its minimum effective concentration and localization could provide important insights into how the spindle facilitates mitosis and where errors might arise. To investigate this, I used microfluidic devices to generate cell-like droplets encapsulating MTgenerating particles, allowing for precise titration of RCC1 concentrations. These devices were fabricated using the soft lithography method, where PDMS with the channel design was bonded to glass coverslips using plasma treatment. The encapsulated RCC1-coated particles facilitate the exchange of RanGDP for RanGTP, establishing a Ran gradient that drives MT formation and spindle assembly. Droplets were formed by injecting Xenopus egg extract as the aqueous phase and HFE-7500 oil as the oil phase. Xenopus egg extract is a cell-free system derived from frog eggs that preserves the biochemical machinery of cell division, making it an ideal model for studying MT dynamics in vitro. HFE-7500 is a fluorinated oil widely used in droplet microfluidics due to its chemical inertness, low solubility of aqueous components, and compatibility with surfactants that maintain droplet stability. My experiments revealed a power-law relationship between the volumetric flow rate ratio of the oil and egg extract and the resulting droplet sizes. Additionally, I observed that larger numbers of smaller RCC1-coated particles produced more complex MT network architectures around the self-localizing particles. To deepen our understanding of how RCC1 drives spindle formation, further experiments using varying concentrations of RanQ69L and RCC1 particles in microfluidic systems are needed.

This research explores the structural and catalytic implications of using melamine cyanurate (MCA), a supramolecular hydrogen-bonded framework, as a novel support material for heterogeneous palladium catalysts. Palladium nanoparticles were supported on MCA using both sol immobilization and impregnation–reduction methods, with additional samples incorporating a sulfur-containing ligand (MTT) to probe surface interactions. Structural, compositional, and morphological analyses were performed using XRD, XPS, TEM, and TGA. Catalyst performance was assessed using the Suzuki–Miyaura cross-coupling reaction. While palladium was successfully incorporated with small particle sizes, MCA-supported catalysts demonstrated low catalytic activity and significant evidence of support degradation under basic reaction conditions. These findings suggest that while MCA offers favorable nanoparticle dispersion and tunability, its structural instability under certain conditions limits its application as a robust catalyst support. Further optimization of reaction conditions and support modifications are proposed to improve its viability.

Cytomegalovirus (CMV) remains a significant global health concern, particularly in immunocompromised individuals and in its propensity to produce serious congenital infections and birth defects. Telomerase, a ribonucleoprotein complex that maintains telomere integrity, has emerged as a novel target in antiviral therapy due to its regulatory role in cell proliferation and virus-host interactions. Prior work from our laboratory (Cavanaugh, et al. 2025) has established that inhibition of host telomerase in human cell cultures is viricidal; this suggests an entirely new approach to anti-viral therapy. This thesis aims to address the engineering challenge of developing a robust murine model to study telomerase inhibition as a treatment for murine cytomegalovirus (MCMV) infection. The first aim is to validate murine cells as a biologically relevant system by determining whether MCMV upregulates telomerase activity, and whether pharmacologic inhibition of telomerase suppresses viral gene expression. This was accomplished through TRAP assays, qPCR of mTERT and IE1 expression, and cytotoxicity profiling of telomerase inhibitors. The second aim is to evaluate and compare the efficacy of four telomerase-inhibiting compounds, MST-312, BIBR-1532, BRACO-19, and RHPS4, by assessing their impact on both telomerase activity and viral gene expression in infected MEFs. This includes determination of LD50 values, quantification of mTERT repression, and analysis of immediate early viral gene (IE1) expression. By quantifying the degree of viral suppression following telomerase inhibition, this study identifies promising candidates for further preclinical testing. Ultimately, this work lays the foundation for a murine model of CMV suppression via telomerase inhibition and offers insights into the translational potential of telomerase-targeting antivirals. It bridges virology, cellular biology, and therapeutic engineering in pursuit of innovative antiviral strategies.

Electrolytic manganese dioxide (EMD) is a critical cathode material in lithium-ion battery technologies, requiring high-purity Mn(II) derived from manganese dioxide (MnO2) ores. Industrial production typically involves reducing Mn(IV) to Mn(II), followed by purification and electrochemical reoxidation. Conventional methods like reductive roasting or hydrogen peroxide leaching pose economic and environmental challenges. This study investigates ozone as an alternative leaching agent and benchmarks its performance against hydrogen peroxide. Synthetic EMD and manganeseenriched pyrolusite ore were suspended in sulfuric acid and treated with either hydrogen peroxide or ozone. UV-Vis and ICP-OES analyses tracked manganese speciation and dissolution over time. Hydrogen peroxide e↵ectively reduced Mn(IV) to Mn(II), with strong UV-Vis and ICP signals confirming dissolution. Ozone produced pink Mn(II)-like filtrates and transient Mn(VII)-like features, but ICP-OES data revealed lower net manganese concentrations, suggesting competing reoxidation or reprecipitation. Mechanistic evidence indicates ozone facilitates manganese dissolution via surface-mediated redox cycling involving reactive oxygen species, rather than acting as a direct reductant. Impurity e↵ects in natural ores further modulate redox balance. These findings highlight both the promise and complexity of ozone-mediated leaching and reinforce hydrogen peroxide’s e↵ectiveness as a benchmark for future process design.

Förster resonance energy transfer (FRET) is a spectroscopy technique that measures energy transfer between acceptor and donor compounds attached to biological molecules. It is a convenient metric for distance measurement on the angstrom scale. Theoretically, this process could be applied to DNA mixtures to identify key components in a variety of applications, such as pathogen detection in crops. The uncertainty inherent in the use of the procedure for identification must be overcome with a similarly stochastic process. This work presents a foundational dataset which serves as a proof-of-concept for the potential to create an unsupervised model that could be used to determine whether detectable patterns arise from FRET efficiency sampling of such mixtures. A coarse-grained DNA model is selected for compatibility with relevant protein models and close approximation of realistic datasets. Through CMA-ES performed on MD simulations of twelve batches of potential acceptor probes that seek to select for interaction with one genome of interest, a basis for synthetic data generation is achieved. Analysis shows that significant changes to the fitness of probes for selectivity of interaction with a specific DNA mixture can occur, showcasing that extraction of patterned information from a synthetic FRET profile for the purpose of mixture identification is plausible. This work provides insight into how machine learning models can be used for the characterization and identification of unknown DNA sequence mixtures while also furthering the advancement of such analysis in dynamic environmental systems like what can be found in field conditions.

This study investigates the effect of nozzle geometry on flow-induced nanostructure alignment in a 50 wt% Pluronic 84 and water solution using birefringence imaging. The original objective was to evaluate alignment behavior in SEBS (styrene-ethylene-butylene-styrene) block copolymers under 3D printing conditions. However, mechanical failure of the 3D-printed nozzles—cracking under the high pressures— necessitated a pivot in material and methodology. Pluronic 84 was selected as a model system. Two nozzle geometries—a conical and a hyperbolic profile—were evaluated across a wide range of flow rates. While the hyperbolic nozzle was expected to induce greater alignment due to smoother extensional flow, both geometries produced comparable alignment levels. This suggests that the highly structured and viscoelastic nature of the Pluronic gel may limit further alignment regardless of flow profile. Instabilities observed in the birefringence images, potentially caused by elastic flow behavior or sample inhomogeneities were further investigated using non-polarized imaging. These findings highlight the interplay between flow geometry and material properties in directing nanostructure orientation. Future work will revisit SEBS with stronger nozzle materials and explore a wider range of pluronic concentrations, and nozzle geometries.

The increasing global demand for hydrogen (H2) as a clean energy carrier underscores the urgency of developing sustainable H2 production methods. Although widely used, conventional steam methane reforming (SMR) is carbon-intensive, spurring the exploration for alternatives. One promising route is dry methane reforming (DMR), which utilizes carbon dioxide (CO2) and methane (CH4), two potent greenhouse gases, to produce syngas (carbon monoxide (CO) + H2). However, DMR's high-temperature requirements and catalyst deactivation via coke formation hinder its practical implementation. This thesis investigates plasma-assisted DMR as a low-temperature alternative, leveraging non-thermal plasma (NTP) to activate reactants at milder conditions. A coaxial dielectric barrier discharge (DBD) reactor was employed to evaluate Pt catalysts supported on alumina (Al2O3), silica (SiO2), and zeolite 3A synthesized via incipient wetness impregnation. Catalyst characterization by N2 physisorption, CO diffuse reflectance IR Fourier transform spectroscopy, transmission electron microscopy, CO pulse chemisorption, and thermogravimetric analysis revealed that Pt dispersion and stability was influenced by the support, with larger surface area supports exhibiting higher Pt dispersion. While Pt/SiO2 had complete selectivity for CO and H2 in thermal DMR, Pt/Al2O3 and Pt/3A also facilitated the reverse water-gas shift (RWGS) reaction, as evidenced by H2O formation. CO and H2 formation rates were enhanced by the NTP even at low temperatures on metalfree and metal-loaded supports, and ethane (C2H6) was also formed, although to a lesser extent when Pt was present. Plasma power and product formation rates on metal-free supports were linked to support dielectric properties, which influenced plasma behavior and homogeneity. Post-reaction analyses demonstrated minimal coking and NP sintering for supported Pt catalysts and, in some cases, improved NP size uniformity. This study demonstrates that combining NTP with engineered Pt catalysts can reduce temperature requirements, produce new products, and mitigate catalyst deactivation in DMR, providing a promising route for decentralized, electrified methane reforming that also serves to valorize CO2. These findings advance the understanding of plasma-catalyst interactions and contribute to the design of sustainable, low-carbon hydrogen production technologies.

TDP-43 is an RNA-binding protein pathophysiologically implicated in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Experiments by Mann et al. have demonstrated doses of "bait oligonucleotides" with high affinity for TDP-43 can abolish neurotoxicity by dissolving pathological TDP-43 condensates in the cytosol [1]. To increase the odds of translational success for this technology, growing and diversifying the group of sequences known to bind TDP-43 with high affinity is desirable. Computational screening is a promising method for prudent identification of sequences to test experimentally, saving time and money. In this thesis, we explore the use of observables from all-atom molecular dynamics simulations to score the bound poses of TDP-43 to various RNA sequences. Coarse-grained unbinding simulations and AlphaFold uncertainty measurements were also examined. The results indicate that all-atom RMSF correlates the best with experimental Kd (Pearson approx. 0.7). AlphaFold also weakly correlated (Pearson approx. 0.5), while coarse-grained simulations possessed no correlation. Temperature replica exchange simulations suggested that vanilla simulations do not fully sample the range of bound conformations for RNA, which could interfere with correlation of observables and Kd. Notably, vanilla simulations were able to persistently model residue interactions of known experimental significance (pi stacking and hydrogen bonding between TDP-43 and RNA), which may have contributed signal to the RSMF correlation. Our results suggest the RMSF has potential for use in a pose scoring function, but that it is insufficient alone to predict Kd. Due to noise in experimental affinity values and poor sampling in vanilla simulations, more high-quality experimental Kd data is imperative, in order to build more complex MD-based scoring functions while avoiding overfitting.

Biomolecular condensates are membraneless organelles inside living cells that primarily comprise proteins and nucleic acids. The thermodynamic process of liquid-liquid phase separation has been proposed as a primary driver of biomolecular condensation, and it is recognized that phase separation is maintained by networks of biomolecular interactions within these liquid droplets. Canonical examples of condensing biomolecules include prion-like low-complexity domains (LCDs) of proteins, and simulations of single-component LCD condensates have predicted the presence of small-world topologies in the interaction networks underlying condensate stability. Recent experimental and theoretical works have also demonstrated inhomogeneities in single-molecule conformation, orientation, and dynamics within biomolecular condensates. Here, we systematically characterize the molecular networks underlying both LCD condensates and condensates formed by generic associative heteropolymers. Further, we investigate the relationship between network topologies and single-molecule properties within condensates. To probe LCD condensates, we employ a chemically specific, coarse-grained model of disordered proteins designed to reproduce phase separation statistics. We generalize our findings by varying sequence hydrophobicities using a generic binary model of associative heteropolymers, dubbed the “hydrophobic–polar” (HP) model. In both model systems, we find persistent small-world topologies underlying single-component condensates. These topologies feature molecular “hubs” with high network betweenness centrality and molecular “cliques” that represent densely interacting clusters of biomolecules; distal cliques in condensate volumes all localize to phase interfaces and are bridged by elongated hubs that remain near condensate centers. Strikingly, we find that relationships between network connectivity and biomolecular structure and dynamics are governed by power laws. Our work demonstrates that inhomogeneous single-molecule behaviors within biomolecular condensates can be well predicted from condensate network connectivities. Furthermore, we find that network cliques have substantially longer lifetimes than molecular hubs, and that the motion of molecules within cliques is spatially constrained. Together, these results reveal a dynamic hub-clique architecture underlying condensates and suggest that the physicochemical characteristics and material properties of phase interfaces are critical to pathological gelation and fibrillization processes observed in condensate aging.

Rising atmospheric CO₂ concentrations in the recent century has highlighted the urgent need for effective carbon capture technologies. Among these, direct air capture (DAC) offers a unique carbon-negative solution by extracting CO₂ directly from ambient air. Traditional DAC approaches using thermal or pressure swings are energy-intensive, prompting interest in moisture swing capture, where CO₂ is absorbed at low humidity and released at high humidity. This study explores the use of cation exchange resins (CERs) as a potential alternative to the commonly studied anion exchange resins (AERs) for moisture swing DAC. By testing CERs with aminophosphonic and iminodiacetic functional groups and comparing them to AERs with phosphate and carbonate counterions, the study investigates CO₂ capture capacities, capture/regeneration rates, and performance across multiple cycles. Results suggest that CERs go through a similar but marginally different mechanism as AERs, where functional groups are hydrolyzed to produce hydroxyl ions, which then react to capture CO2 in the form of bicarbonate. The captured CO2 is released at high humidity as bicarbonate, and the acid form of functional groups neutralizes. However, their CO₂ capture capacity and rates are significantly lower than those of AERs, largely due to differences in hydrolysis mechanisms and functional group pKa values. Nevertheless, CERs demonstrated stable performance over repeated cycles, suggesting potential for improvement and application in scenarios where water purity is a limiting factor. This research offers insights into the feasibility of CERs for low-energy, water-tolerant DAC systems and highlights pathways for further material optimization.

Plant root exudates shape the structure and function of the rhizosphere microbiome by providing chemical cues and substrates that influence microbial survival, colonization, and interaction with the host. These exudates can select for beneficial microbes that support plant growth or suppress immunity, making them critical in mediating plant-microbe interactions. This study focuses on two Arabidopsis thaliana (Arabidopsis) associated, immunity-suppressive bacterial strains, Dyella japonica MF79 (MF79) and Brevundimonas sp. MF374 (MF374), which use distinct genetic mechanisms to suppress root immune responses. To evaluate how the root exudate composition influences bacterial fitness, transposon mutant libraries of MF374 and MF79 were grown in exudates derived from Arabidopsis Col-0, fls2 mutant, and cyp79b2/b3 mutant lines. Genome-wide fitness profiling using random barcode transposon-site sequencing (RB-TnSeq) and Random Forest classification revealed minimal fitness changes in response to flg22 treatment, but strong genotype-specific fitness differences driven by root exudate composition. In MF374, candidate genes involved in osmotic regulation, cell wall synthesis, oxidative stress response, and translation were identified as major contributors to fitness in root exudate environments. Targeted gene deletions confirmed reduced fitness in immune-active or metabolite-rich exudates; however, root colonization assays demonstrated that these fitness effects did not always translate to differences in colonization capacity. This framework enabled the identification and functional assessment of bacterial genes important for exudate-mediated fitness and adaptation. The findings offer insight into how variation in root exudate composition influences the assembly, persistence, and adaptation of immunity-suppressive bacteria within the root microbiome and have broad implications for understanding plant health and developing strategies for microbiome-based agricultural applications.

Bioelectrochemical systems (BESs) interface bacterial metabolic processes with electronic systems for purposes including biosensing, bioremediation, and modulating biological activity. A key bottleneck that currently hinders development of these systems is the difficulty of interfacing bacteria with electronics, resulting in the limited mutual transfer of charge associated with the contact between the bacteria's natural biofilms and an electrode surface. I developed a formulation using biocompatible conductive polymers and designed a device in which to electropolymerize an engineered living hydrogel that incorporates electrically active microbes into its polymer matrix, increasing the surface area contact between the bacteria and the electrode. With a connected potentiostat, the device allows for repeatable, consistent electrochemical systems for hydrogel synthesis and measurement via cyclic voltammetry and chronoamperometry. Engineering a new bridge between biology and electronics requires material that can syncretize the two worlds and an engineering design-build-test-learn cycle that can fine-tune this bridge to create a path toward more environmentally-friendly energy production in the future.

Sustainable biofuels can be produced using agricultural feedstocks rich in lignocellulose, such as corn stalks and barley straw—low-cost, renewable feedstocks that are typically discarded. However, lignocellulose is both physically and chemically challenging to degrade. Anaerocellum bescii, a thermophilic bacterium that thrives at ~75°C, is a promising candidate for biorefining processes due to its powerful arsenal of carbohydrate-active enzymes (CAZymes), which can efficiently break down lignocellulose and convert it to ethanol and other biofuels. However, efforts to metabolically engineer A. bescii are hindered by a limited understanding of its ATP-binding cassette (ABC) sugar transport systems, which play a vital role in importing extracellular oligosaccharides into the cell. The Xylan Degradation Locus (XDL) and Conserved Xylan Utilization Locus (CXUL) are involved in xylose utilization and together encode three putative ABC sugar transporters. The XDL includes AxoFGE (Athe_0174–0176) and XloEFG (Athe_0179–0181), with Athe_0174 and Athe_0181 as the associated substrate binding proteins, while the CXUL gene cluster xynUVW (Athe_0614–0616) encodes Athe_0614. By heterologously expressing and purifying these proteins, followed by ligand screening using differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC), we identified their hemicellulosic sugar preferences and roles in transport. Additionally, ROSIE docking simulations, contact residue mapping, and sequence alignments with homologous proteins revealed conserved amino acids involved in xylo-oligosaccharide recognition. This work advances our understanding of how A. bescii utilizes diverse hemicellulosic substrates, accelerating its development as a platform for sustainable bioproduction of biofuels and other valuable chemicals.

In addition to the onslaught of environmental challenges posed by the warming climate, eutrophication is encouraging the growth of the cyanobacteria that cause harmful algal blooms (HABs) and release toxins that threaten human, wildlife, and environmental health. To track the emergence of and protect against cyanobacterial toxins, as well as to inform remediation processes, a robust and real-time detection system is first required. Here, I design, construct, and evaluate a sensor system that integrates Shewanella oneidensis’ naturally occurring extracellular electron transport (EET) pathways with nanobodies’ capacity for antigen-specific binding to digitize information on contamination events via a modulated electrical signal. This report outlines the expression, purification, and binding-capability assessment of the biological components for such a real-time bioelectronic detection device. In addition to creating a biohybrid device to detect microcystin-LR – a well-characterized cyanobacterial toxin within the microcystin family – I also designed a model system to detect green fluorescent protein (GFP) and validate the approach to cell-surface binding. The final design relied on the direct display of nanobodies on the surface of S. oneidensis for target detection. After confirming target binding to the cells via microscopy, I performed an electrochemical analysis of the sensor in response to target exposure. This preliminary study indicated that the engineered strain of Shewanella underpinning the sensor design demonstrates a change in current distinct from that of the control strain in both chronoamperometry and cyclic voltammetry experiments upon addition of the target.

The ability to spatially control the presentation of proteins on substrates is essential for investigating cellular responses to microenvironmental cues and for engineering functional biomaterials. This paper reviews and compares several protein patterning techniques, including photopatterning using quartz photomasks, digital micromirror devices (DMDs), microcontact stamping, and patterning with stencils. Each method is evaluated based on its throughput, accessibility, and suitability for generating both defined patterns. Each method was tested experimentally, and it was found that the cells were able to adhere onto substrates regardless of where the protein patterns were created for techniques that used Poly-L-lysine polyethylene glycol (PLL-g-PEG). In order to more reliably control the protein pattern and cell adhesion, physical barriers such as silicone stencils were also used to dictate the protein patterning. This allowed for easy and reliable control over the protein patterns, which enabled the electrotaxis experiment that was conducted. The protein patterned substrates are used to control the shape and size of cells to study the effect of tissue size on the response of the tissue when placed in an electric field. We found that increasing tissue size leads to an increase in the velocity of the cells along the direction of the stimulation direction, and we provide several explanations for why this phenomenon is observed.

The rhizosphere is a dynamic chemical environment shaped by secondary metabolites that influence microbial interactions, nutrient availability, and plant health. Among these, ribosomally synthesized, post-translationally modified peptides (RiPPs) represent a largely unexplored class of natural products. DUF692 multinuclear non-heme iron-dependent oxidative enzymes (MNIOs) are an emerging family of tailoring enzymes involved in the biosynthesis of RiPPs. These MNIO-modified RiPPs have shown growing evidence for roles in copper chelation, antivirulence functions, and microbial community dynamics, though their full potential remains underexplored. To address this gap, this study presents a scalable bioinformatic pipeline for identifying MNIO-associated biosynthetic gene clusters (BGCs) across bacterial genomes. Application of this framework uncovered over 1,200 putative MNIO-RiPP BGCs. To probe their functional relevance, we selected a candidate cluster from [Actinomadura] parvosata subsp. kistnae for experimental study. Mass spectrometry analysis revealed a unique mass shift consistent with MNIO-mediated modification, and a putative structure has been proposed. This work expands the known chemical space of MNIO-RiPPs and underscores their potential as bioactive mediators of rhizosphere interactions with translational relevance in environmental and agricultural contexts.

Chondroitinase ABC is an enzyme that has shown therapeutic potential in treating spinal cord injuries through the breakdown of glial scarring, which inhibits axonal regrowth. However, it is unstable at human body temperature, and has been difficult to stabilize, which makes it challenging to implement as a therapeutic. Thus, a collaboration between the Webb and Gormley labs identified and tested the ability of copolymers to serve as stabilizing agents. Many of these copolymers successfully boosted the retained enzyme activity (REA) of chABC over 24 hours at 310K, or human body temperature. However, the experiments involved in this study did not reveal the mechanism by which these copolymers endowed thermodynamic stability. If this mechanism of stabilization were better understood, it would contribute to the understanding of chABC’s thermal instabilities and forward the goal of implementing chABC as a therapeutic. Molecular dynamics simulations were utilized to computationally interpret the mechanism of copolymer stabilization of chABC. These simulations revealed that there are facets of chABC’s wet lab behavior that can be understood and interpreted computationally, as well as contribute to the understanding of copolymer chABC stabilization. Investigating the interactions between chABC and high REA copolymers revealed that high levels of intermolecular contact can lead to a maintained protein structure, while low REA copolymers can exhibit distinctly destabilizing behavior not exhibited in a laboratory setting. These impacts may be related to the ability of copolymers to block the collapse of chABC from a crescent into a torus. Successful blocking of destabilizing intra-protein interactions, along with high contact bracing, seems to result in the most successful stabilization of chABC. Conversely, blocking the destabilizing intra-protein interactions without any additional support results in the most significant destabilization of chABC.

Biofuel development can serve as a possible approach to reduce GHG emissions and help mitigate the effects of climate change on the environment and human health. In the development of biofuels involving microorganisms, there have been various trade-offs between growth and ethanol production. While previous studies have proposed different approaches to assess these trade-offs, temperature-sensitive (ts) mutants have not been widely explored in this context. These mutants lack the function of a certain essential gene at nonpermissive temperatures and display full to partial function at semi-permissive temperatures. In this project, we performed screenings on a library collection consisting of 600 temperature-sensitive mutants of Saccharomyces cerevisiae essential genes at permissive (21°C) and nonpermissive temperatures (38°C). The aim is to identify strains that exhibit impaired growth at elevated temperatures, but can still maintain active metabolism to efficiently produce ethanol. This is based on the assumption that high temperatures can disrupt cellular processes and functions linked to growth, but not ethanol fermentation because of the changes in metabolic pathways. Initial screenings were conducted using alcohol dehydrogenase assays, and high-performance liquid chromatography was used to verify the high specific production ratios of 15 ts mutants that were selected. The gene ontology analysis confirmed that many of these selected genes are involved in growth-related processes, such as cell cycle regulation and ribosomal biogenesis. Further work should be conducted to dynamically control these strains using optogenetic techniques to improve the fermentation efficiency. Additional experiments should also investigate whether these ts alleles can produce more ethanol than the wild-type (BY4741) through fed-batch fermentations.

Understanding how antibiotics affect bacterial cells beyond their primary target is essential for developing next-generation therapeutics. Traditional profiling methods primarily focus on identifying a drug’s direct target, often overlooking the downstream cellular effects of antibiotic treatment. This study evaluates a modified Bacterial Cytological Profiling (BCP) approach that uses a panel of Escherichia coli mutants with deletions in transcription-associated genes to characterize antibiotic mechanisms of action beyond their primary target. By integrating GFP-based translational readouts with measurements of DNA content (DAPI), membrane structure (FM4-64FX), and cell size (FSC), analyzed via flow cytometry, we examined cellular responses to two transcription-blocking rifamycins: rifampicin (RIF) and rifabutin (RFB).

We found that many mutants exhibited consistent and gene-specific cytological responses to RIF, highlighting the method’s ability to reveal downstream effects of RNAP inhibition. Additionally, drug-specific phenotypes emerged between RIF and RFB, demonstrating that the modified BCP approach can resolve subtle mechanistic differences between closely related antibiotics.

Together, these findings validate the modified BCP approach as a tool for uncov- ering antibiotic action with greater depth and resolution, offering valuable insights for future drug development.