Undergraduate Research Program

Therese Pacio

Major: Computer Science
Mentor: Fred Mast, PhD and John Aitchison, PhD ; Seattle Children’s Research Institute: Center for Global Infectious Disease

Contact: tpacio@uw.edu

Current research project: Modeling kinase networks involved in peroxisome biogenesis via single-cell colocalization and morphology metrics

Therese is a sophomore majoring in computer science with a track in computational biology. She is passionate about the intersection between computation and medicine, and she hopes to develop technology that elevates medical research and patient-care. She started her research in the Aitchison Lab at Seattle Children’s in the summer of 2020. Under the mentorship of Dr. Fred Mast, she develops python-based image analysis pipelines to quantitatively phenotype cells in microscopy images. She is particularly interested in this research because it utilizes her interests in computer vision to investigate biological dynamics. She looks forward to exploring her passion for computational biology and hopes to contribute to a safe and equitable integration of technology within patient-care.


Translate your work so that we can all understand its importance
Peroxisomes are essential for human-health and its dysfunction can lead to severe metabolic disorders. Understanding the biological networks that contribute to the formation of peroxisomes as well as its role in other pathologies is critical for developing new treatments. While a spatiotemporal model of peroxisome biogenesis has been characterized in yeast cells, the extent of conservation of this model in humans remains unknown. To address this problem, scientists in the Aitchison lab utilized high-throughput fluorescent microscopy techniques to generate large datasets consisting of multi-dimensional cell images that examined peroxisome state in the presence of certain kinase-inhibitors (cancer treatments). My research involves developing CLARITY, the computational image analysis pipeline to analyze these datasets. They consist of terabytes of data, so commercial software is not feasible for data analysis. Furthermore, a single image consists of hundreds of cells. Cells are highly dynamic, so two cells in the same treatment may not be in identical states. Most image analysis pipelines fail to quantify meaningful differences between cells in a single image because they compute only a single statistical value per image. CLARITY utilizes computer vision techniques to extract single-cell features from multi-channel and multi-dimensional images consisting of hundreds of cells. We have utilized CLARITY to measure differences in peroxisome colocalization and morphology in response to different kinase-inhibitors. We look forward to using this data to propose potential networks of kinases that may be involved in peroxisome biogenesis.



When, how, and why did you get involved in undergraduate research?
I started my undergraduate research experience through the UW GenOM ALVA program and the Seattle Children’s URM internship during the summer of 2020. At the time, I had just recently found a passion for computer science, so I was fairly new to computational research. Computer science is a large field with multitudes of applications, so I was unsure of how I wanted to contribute to the field. My research has allowed me to find my passion for computer vision and has shown me how my curriculum can directly serve my research interests! It has made me a much more engaged and excited student!


What advice would you give a student who is considering getting involved in undergraduate research?
I would advise the student to open their mind to all kinds of research experiences! Research is a great way to find what you are passionate about and you never know what will really spark your interest!