Finding Patterns in Complex Biology

Fibromyalgia is a chronic pain condition that disproportionately affects women, yet it’s still very poorly understood. We don’t fully understand the biological mechanisms behind it, which also makes it difficult to develop treatments that directly target the underlying cause of the condition. 

This summer, I have been working remotely with a lab at Johns Hopkins University that prioritizes developing alternatives to animal testing in research. That was the main reason I chose to intern with this lab. I wanted the opportunity to work on a disease that I find especially interesting while also learning research methods that can help answer biological questions without relying on animal models. 

Most of my work has been computational, so instead of generating new data in a laboratory, I work with large biological datasets that already exist. I have worked with genetic data and ribonucleic acid sequencing data, and I am also beginning to work with proteomics and other types of biological information. 

My goal is to take these different types of data and connect them in a way that helps tell a biological story about fibromyalgia. No single dataset can explain the whole disease, but different kinds of evidence can point toward genes, proteins, pathways or biological processes that may be involved. I am interested in seeing how those pieces fit together and whether they can help build a more evidence-based hypothesis about what might be happening biologically in fibromyalgia. 

My internship also connects very directly to what I study as a Biochemistry, Biophysics and Molecular Biology major at Whitman. In my classes, I have learned about genes, proteins, molecular pathways and disease, but working with real research data has been very different from working through an example in a textbook. Real data are messy. There is a lot to learn, and there are a lot of things that go wrong during analyses. 

That learning process, and all of the challenges I ran into as a beginner in computational research, eventually led me to another project that I am especially proud of: a developing research workflow operating system for biostatistics and computational biology. I started developing it because I wanted a better way to keep track of the research process and all of the different pieces that come with it. It is still developing, but the idea is to create a system that can guide someone through research while also organizing things like literature, datasets, analyses, figures, decisions and the problems that come up along the way.

A lot of the features I want to include come directly from things I struggled with myself. I have found that the hardest part of computational research is figuring out what step comes next, why I’m doing it, what I’m supposed to save and how everything fits together. I want this OS to eventually make that process more approachable for someone who is starting from the beginning, while also encouraging better organization and reproducibility in research. 

This internship also supports my long-term goal of working in drug design. Before you can think about designing a drug, you first need to understand the disease you are trying to treat. You need to know what biological processes are going wrong and what genes, proteins or pathways might actually make sense to target. Through this internship, I am learning how researchers can use existing biological data to begin answering those questions. 

I hope that by the end of this experience I will not only have a better understanding of fibromyalgia, but also be much more confident working independently with computational research methods. I also hope the work I am doing can contribute to the lab’s broader effort to develop useful research approaches that reduce reliance on animal testing. With fibromyalgia specifically, even identifying stronger biological hypotheses can help narrow down what researchers should investigate next. 

One of the biggest things I have learned this summer is that being a beginner is not a reason not to apply for an opportunity. I started this internship knowing that there were a lot of methods I had never used before. I still have a lot to learn, but that is also the point of doing an internship. 

My advice to other students is to look for opportunities that genuinely interest you, even if you do not feel completely qualified yet. Ask questions, be willing to get things wrong, and do not assume that struggling with something means you are bad at it. Sometimes struggling with a process is exactly what teaches you how to improve it. 

If you are interested in gaining research or career experience of your own, I would encourage you to check out the Whitman Internship Grant webpage, read other students’ internship stories or connect with the Career and Community Engagement Center. You do not need to have your entire career figured out before you start exploring what kind of work you might want to do. 

Bassant Said ’27 (she/her) is a Davis UWC Scholar from Cairo, Egypt, who is majoring in Biochemistry, Biophysics and Molecular Biology. She spent the summer conducting computational fibromyalgia research with Johns Hopkins University. Her summer research started with questions about chronic pain and somehow also turned into designing a research workflow system because she kept thinking, “There has to be a better way to do this.”