Applied Science

Session I

Rudra Aamidala, "Enhancing the Longevity of Recombinant Protein Expression in E. coli Using Temperature-Conditional Synthetic Addiction"

Genetically engineered microbes contribute to biomanufacturing, therapeutics, and bioremediation, or environmental cleanup, but genetic circuits, or engineered gene sets, can gradually lose function because mutations reducing gene expression can give cells a growth advantage. Our project asks whether conditional synthetic addiction can preserve circuit function while reducing the growth advantage of mutant cells. Synthetic addiction links an essential gene to a circuit, requiring cells to maintain circuit activity to remain viable. We are engineering an Escherichia coli system in which this dependency is controlled by temperature, with the circuit induced at higher temperature and uninduced at lower temperature. We will compare conditionally addicted cells with controls by measuring cell growth, fluorescence as an indicator of circuit activity, and stability during repeated culturing. We anticipate conditional control will maintain circuit function longer while improving growth during uninduced periods. This approach could improve engineered microbe stability and make biotechnology applications more practical. 

Student Major(s)/Minor: Computational & Applied Mathematics & Statistics (Mathematical Biology) Major

Advisor: Dr. Margaret Saha

Jeremy Coonley, "Predictive Modeling of Genetic Circuit Longevity From Gene Expression Experiments"

Synthetic biology has produced an impressive array of engineered genetic circuits, with applications in human health, environmental remediation, agriculture, and biotechnology. However, far less attention has been placed on what happens after initial circuit function, with few examples of circuits undergoing long-term testing and failure analysis. While several mechanisms of circuit failure are already known, including mutation, selection, metabolic burden, and environmental interactions, there are limited attempts of mathematical frameworks for predicting time until failure that have been verified with long-term experimental data. This research project will explore the loss of circuit function over time through a deterministic ordinary differential equation model based on longitudinal RNA-seq data from three circuits that have gone through extensive reliability testing in perfect laboratory environments. Additionally, with the goal to eventually deploy these circuits in the real world, this model will be expanded to explore the impact of environmental factors on circuit function over time. 

Student Major(s)/Minor: Applied Mathematics and Computer Science Major

Advisor: Dr. Margaret Saha

Lillian Dang, "Evaluating the Evolutionary Stability and Longevity of Engineered Kill Switches"

In synthetic biology, safely deploying genetically modified organisms requires biosafety controls, primarily “kill switches", engineered genetic circuits designed to trigger cell death and prevent accidental environmental release. However, despite their widespread use, these kill switches fail over time, requiring focused study to identify their failure points so they can be improved uponThis research investigates the longevity of kill switches and the genetic mechanisms driving their breakdown over time. Escherichia coli carrying toxin-based kill switch plasmids were continuously cultured in log phase through twice daily serial passaging over 15 days. Daily induction assays, colony forming unit (CFU) counts, and DNA and RNA sequencing were conducted to track functional decline. Results show a progressive decrease in kill switch efficiency, with complete failure observed by Day 15. Sequencing is expected to reveal specific survival mutations and gradual decline in toxin gene expression. Understanding these failure pathways is important for designing durable biosafety mechanisms for real world applications.

Student Major(s)/Minor: Data Science Major

Advisor: Dr. Margaret Saha

Israt Farah, "Isolation, Biobanking, and Whole-Genome Sequencing of Fungi from Vulnerable Coastal Environments"

This project will investigate fungal biodiversity across diverse natural environments through systematic field collection and documentation. Fungi play lots of important roles in ecosystems and have contributed to major medical discoveries like antibiotics and dandruff treatment. However, fungi has often been overlooked as an area to research, despite such revelations, and has remained understudied and undocumented when compared to bacteria and other organisms. The central research question asks how field-based research and cataloging help improve understanding of fungal diversity and identify species with potential ecological or biomedical significance.

The project's expansion of existing documentation for different species of fungi will help to advance biodiversity studies, ongoing ecological assessments, and long-term fungal identification for their potential medicinal or environmental use. The creation of a fungal library will therefore allow scientists in the future to conduct their research using the data contained in that library, as well as develop a better understanding of how diverse fungi are in nature.

Student Major(s)/Minor: Data Science Major, Mathematics Minor

Advisor: Dr. Geoffrey Zahn

Sophia Jagels, "A CRISPRi-Mediated In Vivo Biosensor of Circuit Dysfunction"

The rapidly expanding field of synthetic biology has created a vast array of genetic circuits for use in environmental sustainability, therapeutics, and biomanufacturing.  Many of these circuits, however, lack rigorous failure analysis, especially in real-world conditions. To address this foundational gap, a genetic "check engine light" (biosensor) was developed to detect circuit failure in E. coli. The system utilizes gene-silencing CRISPR technology (CRISPRi) to repress a red fluorescent protein reporter. Under normal operation, the circuit actively silences fluorescence; upon circuit failure, this repression ceases, causing the bacteria to glow red proportionally to the circuit’s degree of dysfunction. Sequencing confirmed successful assembly of the biosensor’s genetic components, which consist of a constantly expressed genetic circuit and a gene encoding a CRISPRi protein. Once functional testing is complete, this biosensor will provide researchers real-time feedback on circuit stability, paving the way for safer, more reliable deployments of synthetic biology in real-world applications.

Student Major(s)/Minor: Biology Major, Data Science Minor

Advisor: Dr. Margaret Saha

Sneha Mukku, "Investigating the Role of the tweety 3 Gene in Embryonic Development"

The Tweety gene family, consisting of ttyh1, ttyh2, and ttyh3, codes for evolutionarily conserved membrane proteins involved in cellular processes that include cell division and early neural development. These genes have been shown to have clinical relevance, with ttyh1 being associated with numerous diseases and cancers, and ttyh3 being found to push the proliferation and metastasis of tumor cells. However, the expression patterns of ttyh3 during early development are not completely known. This summer research project will continue to ask the question: how does the overexpression and knockout of ttyh3 (tweety homolog 3) affect the spatial and temporal patterning in Xenopus laevis during early embryonic development. Previous research suggests that there are strong ttyh3 signals throughout the anterior nervous system with signals extending to the spinal cord by the tailbud stages. This research will aim to replicate these results and further study the spatial and temporal patterning that the overexpression and knockout of ttyh3 will cause. Embryos will be collected from Xenopus laevis matings and will be used to nanoinject mRNA of ttyh3 or a ribonucleoprotein consisting of the Cas9 protein bound to my designed single guide RNA (sgRNA) molecule into the 2 cell stage either unilaterally or bilaterally. This research will increase scientific understanding of the Tweety gene family and their expression patterns during early development. 

Student Major(s)/Minor: Biology and Economics Major

Advisor: Dr. Margaret Saha

Session II

Hannah Daniel, "Utilizing an Optogenetic Conditional Synthetic Addiction System to Extend Genetic Circuit Longevity and Performance in E. coli"

Synthetic biology genetic circuits have impressive potential to solve real-world problems in a multitude of fields. However, genetic circuits can lose function over time due to metabolic burden, mutation, and evolutionary selection. This project aims to explore whether an oscillating, blue-light conditional synthetic addiction system could prevent circuit breakage and plasmid loss. Synthetic addiction constructs involve coupling an essential and functional gene in a host cell with a chromosomal essential gene deletion, forcing the cell to maintain the construct to survive but also imposing burden on the cell. The implementation of an addiction operon controlled by a blue-light inducible promoter allows for toggled gene expression to optimize selection of plasmid and also alleviate burden. Circuit expression will be measured by taking fluorescence readings throughout longitudinal serial passaging experiments. Understanding circuit breakage and prevention mechanisms will allow for the more practical and reliable deployment of genetic circuits for their intended applications. 

Student Major(s)/Minor: Biology and Data Science Major

Advisor: Dr. Margaret Saha

Scarlett Jensen, "Mining the Fungal Literature to Improve Trait-Based Prediction of Microbiome Assembly"

This project will explore the ability of microbiome transplantation to protect plants from disease, thereby improving ecosystems and preventing the extinction of vulnerable plant populations. Despite previous success, the lack of predictive tools to determine which fungal taxa will successfully colonize their hosts limits broader application. Graph neural networks (GNNs), a type of deep learning, offer the potential to model complex microbial interactions, but their predictive performance depends on the availability of trait data. For fungi, such data remain lacking, as many traits are missing from existing databases and are instead dispersed across historical and contemporary fungal literature. This project proposes a pipeline to adapt large language models (LLMs) to systematically extract missing trait information from the published mycology literature and builds a new trait matrix that integrates existing trait databases. The resulting dataset will be used to improve the performance of models for predicting community assembly. 

Student Major(s)/Minor: Computational & Applied Mathematics & Statistics and Chinese Studies Major

Advisor: Dr. Geoffrey Zahn

Marco Mieles-Caceres, "Computational Modeling of Liquid–Liquid Phase Separation in Dendritic Spine Geometries Using Flory–Huggins Cahn–Hilliard Theory"

We explore the use of finite-element software MOOSE to model liquid-liquid phase separation using Flory-Huggins and Cahn- Hilliard theories. MOOSE enables the investigation of phase separation dynamics across varying geometries, enabling the simulation of a two-variable system to evolve in time and space. Using Gmsh, we develop increasingly complex geometries ranging from one-dimensional to three-dimensional domains to eventually represent a neuronal dendritic spine. The models are developed progressively, beginning with the Cahn-Hilliard equation in simple geometries before incorporating Flory-Huggins free energy formulation to investigate the effects of molecular interaction parameters and initial concentration on phase separation. Boundary conditions are examined to determine their influence on the distribution and evolution of the separated phases. Resulting simulations demonstrate MOOSE ability to capture phase- separating behaviors across multiple pathways and provide a pathway for extending phase-field models to complex biological structures. This work demonstrate the potential of combining finite element methods with phase field theory to investigate complex spatiotemporal processes in a biological system and help provide a foundation for future studies of phase separation.

Student Major(s)/Minor: Computational & Applied Mathematics & Statistics (Mathematical Biology) Major, Biochemistry Minor

Advisor: Dr. Greg Conradi Smith

Travis Wolfe-Thompson, "Longitudinal Failure Analysis of Genetic Circuits"

Genetic circuits are networks of genes and regulatory elements engineered to control cellular behavior. These circuits place a burden on their host cells by channeling resources away from proliferation. This creates an evolutionary pressure to break the circuit. The genetic circuit analyzed in this study was a plasmid facilitating beta-carotene production in E. coli. To analyze how this circuit broke, a longitudinal study involving functionality analysis and RNA-sequencing was performed. Preliminary results show beta-carotene production for up to a month, indicating long-term stability of the circuit. Further analysis of RNA-sequencing data will provide valuable insight into the underlying mechanisms responsible for circuit breakage.

Student Major(s)/Minor: Computational & Applied Mathematics & Statistics (Mathematical Biology)

Advisor: Dr. Margaret Saha

Video Presentations