My Time at MIT – Summer 2025

When I first arrived at the Koch Institute, a research facility at MIT, I expected to spend all my time analyzing tumor growth and dissecting mice. I falsely assumed that, as a cancer research lab, there wouldn't be much diversion from the purely biological elements of the study. I certainly wasn't expecting my computer science knowledge to become my most useful attribute.

On my first day of the internship, I spent most of my time observing and catching up on the lab's most recent research. I witnessed the extraction of a mouse's lymph nodes, learned the purpose of different lab equipment and machines, and read a report about the effect of post-fast refeeding on the immune system. Later, I took notes for the lab on the quality of colonoscopy procedures on a group of mice. However, on the train home, as I reflected upon the fascinating experiments I saw at the lab, my mind kept wandering to one specific moment. It was of a lab assistant, who, out of the corner of my eye, I saw writing code for an algorithm. I couldn't help but wonder what he was working on.

The next day, I decided to approach the lab assistant to ask him about his role. An undergraduate student from Shanghai, he was quite excited to let me in on the program he was building. He introduced me to RNA sequencing, a process where one can analyze the RNA in a sample to create conclusions regarding gene expression. For example, by comparing the RNA of a sample of mice with cancer to healthy ones, we can learn which genes are significantly altered by the sickness. The role of the program was to create organized tables, volcano plots, or heatmaps from the data, enabling researchers to interpret it. Curious to know more, I asked him if I could learn the process myself, and he was, thankfully, quite eager to take me on as his student. Thus began my role as a data specialist at MIT.

The first step to my learning process was getting comfortable with the coding language R, which I had never worked with before. R, I was grateful to learn, is actually quite similar to Python in terms of coding structure and commands––if not easier to work with due to its vast statistic-driven libraries. It took me just about an hour to know my way around base R due to my existent experience in Python. The difficult part was learning how to use DESeq2, a specific library that specializes in RNA Sequencing data. After three long YouTube videos and assistance from my newfound mentor, I was successfully able to create a complete analysis for a dummy data set. Later, on my own, I was able to achieve similar results for a sample of four groups of mice––mutated, iron-supplemented, both, or neither. Eventually, I became so immersed in the analyzation process that the researchers started handing me other datasets to work with––not just for practice, but to compare with their own results.

The most satisfying part of the process was seeing my hard work turn into functioning results. By analyzing the volcano plot, even one who doesn't know much about biology can come to an understanding about what changed between the different groups of mice. For example, the volcano plot expresses the relationship between Log2FoldChange and -log10P, two values given to us through the "results" function in DESeq2. In short, this relationship maps out the genes based on how much change they underwent in gene expression and how likely it was that those changes were due to random errors. The gene "gsta2," for instance, seems to have changed significantly, and not by error. If you took the time to research this gene and its relationship to PDAC (a type of pancreatic cancer) or a boost in iron diets, you would find that this change makes biological sense.

Overall, I found the opportunity to work with researchers at MIT to be an excellent way for me to exercise my computer science skills and practice analyzation on a broader scientific scale. Most importantly, I learned that by clearly communicating my interests, I can find thoughtful people who are willing to help me grow.

*Please see the projects/research column for more details