Nature & Coding: IBM Computational Biologist on @GreekGirlsCode
“Do what you love and don’t be afraid to fail.”
Greek computational biologist and a busy mom of a toddler, Marianna Rapsomaniki remembers her childhood well — and the stories her dad, a captain, used to tell her as a kid. They were all about his travels, about the Universe, and about believing that if you want something really, really badly, your wish will always come true.
Her wish has always been to work with nature — as well as with math and computers.
Today, Marianna works at IBM Research Europe. For a week, she’s taking over the @GreekGirlsCode Twitter account that aims to “inspire girls and young women in Greece to get involved in science and tech” — to tell the audience about her research, some recent breakthroughs in computational biology, and her dreams.
We’ve caught up with her to get to know her a little better.
Q: Computational biology is a fairly new area of research, only gaining traction since the late 1990s. Why did you choose to get into this field?
Before finishing high school, I had to choose an academic path, as we all do. I was torn between studying biology and computer science. Biology is the study of all living things — what’s more fascinating than that? But growing up in a small town in Greece, it wasn’t clear to me how to make a career out of biology.
Afterall, the only biologists I knew were my schoolteachers. A degree in computer science, however, seemed a more reasonable career choice. So I got into engineering at the University of Patras.
Along the way, I discovered computational biology. Then everything clicked: I found a way to combine my passion for biology with all that I had learned through studying computer science. You could imagine how thrilled I was to realize that I could use math, models and algorithms to understand how human cells work, how diseases emerge in our bodies or how to design more effective treatments.
Q: What is the one scientific discovery you are most proud of?
Definitely the work I did with the Bodenmiller Lab at the University of Zurich on developing a single-cell atlas for breast cancer heterogeneity. In this collaborative project, we figured out a way to annotate cellular and phenotypic diversity in breast cancer ecosystems, using mass cytometry, which enables the measurement of over 40 parameters in millions of cells simultaneously at the single-cell level, and machine learning to identify and classify tumor and immune cell types as well as their relationships.
Our work laid the foundation for future precision medicine approaches that could potentially help patients win the fight against breast cancer. I am also very proud of the fact this research is featured in the peer-reviewed journal, Cell.
Q: Were there any particular challenges you faced in this research?
The project was quite complex, ranging from data generation, analysis and interpretation. The difficult part was the data analysis — the dataset was one of the most challenging to analyze, so we had to come up with new approaches. I am extremely grateful to my colleague Johanna Wagner from the Bodenmiller lab for driving this study and taking the time to carefully answer all my questions about breast cancer biology.
Q: In your experience was there once a project that ended up going in a different direction than originally planned?
I first joined IBM Research Europe as part of MetastasiX, an interdisciplinary consortium on breast cancer heterogeneity.When we started analyzing single-cell proteomic datasets, we realized that the measurements were confounded by how the cells grow and divide, in other words their cell cycle.
This led to the creation of CellCycleTRACER, a supervised machine-learning algorithm that classifies and sorts single-cell mass cytometry data according to their cell cycle. The algorithm, in turn, allowed us to accurately identify cell-cycle-state and cell-volume heterogeneity in mass cytometry data. We’ve since made the tool available to the research community for free via the IBM Cloud.
Q: What or who inspired you to pursue a science career?
My dad was a huge influence. He was a captain, and he would always tell me stories about his travels, the world and the cosmos, which totally fascinated me as a kid. Although he never attended university himself, he is one of the most knowledgeable people I know. Even now, he still enjoys explaining difficult concepts in astronomy or cosmology to me. My brother and I always admired him.
Later in high school, I had a gifted math teacher and mentor who made me fall in love with math.
Q: Why IBM Research and what’s it like?
I first came to Switzerland as a visiting PhD student on a Swiss Government Excellence Scholarship. I spent almost half of my PhD in the Automatic Control Lab at the Swiss Federal Institute of Technology (ETHZ), where I had the great pleasure of being supervised by Professor John Lygeros.
After I graduated, I came across a very interesting postdoc ad at the newly-founded Systems Biology group at IBMResearch Europe, Zurich lab. Although I wasn’t initially planning to leave academia, the project exactly fit my scientific goals. Six years later, and I couldn’t be happier.
IBM Research offers a very dynamic and open community of passionate researchers from so many different fields, from nanotechnology to quantum computing and computational biology. I love the interdisciplinary environment as well as thecommunity through which I have built strong friendships over the years.
Q: Do you miss Greece?
Of course, I miss my family and friends, the food, the sun, and the sea! I spent the first 18 years of my life on the beautiful island of Corfu, surrounded by amazing nature and beaches, so I always feel a bit homesick.
But Zurich is undeniably a very beautiful city with a high quality of life. I love the fact that the river is so clean you can actually swim in the city center.
Q: What’s it like to be a mom and a scientist? How do these roles influence each other?
Being the mom of a young toddler during a pandemic has definitely been challenging on multiple levels. Before I had my son I was often working weird hours (I am a night owl) but now I had to learn how to work efficiently, so that I can have quality time with my family.
Motherhood however equips you with many extra abilities: you take multitasking to the next level and learn to be more patient and creative, which are definitely great skills for a researcher.
Q: Any hobbies that help you keep a work-life balance?
I spend most of my free time with my family. I love playing with my son — he is really into puzzles and books right now. During the pandemic we also got into gardening, which I find very relaxing. On the rare occasions I have some ‘me’ time, I love experimenting with my more artistic side, and enjoy watercolor painting, crafting and DIYs, and anything related to interior design.
Q: You have accomplished a great deal throughout your career, what keeps you going, and what’s next?
In biology, the more you learn, the more you realize how much you don’t know, and this drives me to keep pushing. My biggest goal moving forward is to make an important scientific contribution in the field of cancer systems biology. I would also like to continue mentoring students, which is the part of my job I enjoy the most.
Q: Do you have a mentor yourself?
My PhD advisor, Professor Zoi Lygerou. She is one of the most hard working scientists I know, and at the same time a great teacher and mentor. I have great admiration for her strong work ethic, respect for colleagues and collaborators, and strong academic integrity.
Q: What advice would you give to younger generations looking to pursue a career in science?
It might seem cliché, but the most important thing is to do what you love and what drives you. Talk to as many people as you can, learn what they do and you will find ideas in the most unexpected places. Don’t worry about quick results and don’t be impatient, science needs time and persistence. And definitely don’t be afraid to fail.
This week, Marianna Rapsomaniki is taking over the Greek Girls Code Twitter account. Follow her story @GreekGirlsCode
