Women in Research #LINO75: Jana Braunger

Jana from Germany is a PhD student at the Centre for Organismal Studies at Heidelberg University, Germany. She is interested in uncovering the fundamental mechanisms by which genes control cellular behaviour and contribute to disease. To study these questions, she develops machine learning methods for analyzing pooled single-cell CRISPR screening data, where genes are systematically knocked down to investigate their effects on individual cells. Because these datasets are often noisy and difficult to interpret, her work focuses on making such analyses more robust and interpretable in order to better understand cellular responses to genetic perturbations and the underlying regulatory mechanisms.

Jana participated in the 75^th Lindau Nobel Laureate Meeting (#LINO75 – Interdisciplinary).

Enjoy the interview with Jana and get inspired:

What inspired you to pursue a career in science / in your discipline?

My interest in science started quite early through a student research center (“Schülerforschungszentrum”) that I joined in fifth grade. In the beginning, I mostly enjoyed solving mathematical puzzles together with other students, but over time I started developing my own small research projects across different subjects and participated several times in the “Jugend forscht” competition. What fascinated me most was not only the projects themselves, but also the atmosphere: suddenly I was surrounded by other students who were genuinely excited about exploring questions and building something of their own. That felt very different from regular school and showed me how creative and collaborative science can be. Through these experiences, it became clear to me that I wanted to pursue a career in the life sciences. At that point, the question was less whether I wanted to do science and more which field would best combine my interdisciplinary interests in biology, mathematics, and computation. Because these opportunities were so important for my own development, I now mentor students myself at the Heidelberg Life Science Lab. I hope to encourage young students in the same way my mentors once inspired me.

Who are your role models?

I would not say that I have one single role model. Instead, I have been inspired by many different people throughout my journey, often by people who were just one step ahead of me at a particular stage. Early on, my father, who is a biochemist, sparked my curiosity for science and encouraged me along the way. Later, mentors from “Jugend forscht”, supervisors during internships, and researchers I worked with along the way showed me very different ways of approaching science and mentorship. What inspired me most was seeing people who combined scientific excellence with enthusiasm, kindness, and a genuine interest in supporting younger students – mentors who not only discussed project details, but also openly shared advice about career paths and future opportunities.

How did you get to where you are in your career path?

After my early experiences with “Jugend forscht”, I knew that I wanted to go into the life sciences, but I was equally fascinated by mathematics and interdisciplinary problem solving. That led me to study Molecular Biotechnology at Heidelberg University, where I gradually became more interested in computational approaches to biological questions. A major turning point was my bachelor’s thesis, when I decided to try a fully computational project instead of working in the wet lab. I quickly realized how much I enjoyed bioinformatics and data-driven research. To strengthen my quantitative background, I then pursued a master’s degree in Biomathematics at the University of Greifswald and later joined the lab of Caroline Uhler at the Broad Institute of MIT and Harvard in Boston for my master’s thesis. Working in such an interdisciplinary research environment further confirmed my interest in computational biology and method development. For my PhD, I joined the newly established group of my former bachelor’s thesis supervisor, Britta Velten, at Heidelberg University, which felt like a perfect fit both scientifically and personally.

What is the coolest project you have worked on and why?

Some of the most memorable projects I worked on were actually my early “Jugend forscht” projects, because they were often connected to very tangible everyday questions and easy to explain to other people. I loved the freedom to explore completely different topics together with other motivated students — from analysing whether soy products contained genetically modified ingredients to developing computational approaches for hospital shift scheduling. One of my funniest memories is from our first attempts at gel electrophoresis in the lab. We quite confidently assembled everything ourselves for the first time and only later realized that we had set it up in the wrong direction. Even though many experiments did not work immediately, those moments made research exciting and taught me that mistakes and troubleshooting are simply part of science. At the same time, experiences like these also helped me realize that I personally enjoyed computational research even more than wet lab work. That became especially clear during my bachelor’s thesis, which was my first fully bioinformatics-focused project and confirmed that this was the direction I wanted to pursue further.

What’s a time you felt immense pride in yourself / your work?

One of the most rewarding moments for me was realizing that other researchers were genuinely interested in using a Python package I developed at the beginning of my PhD. Seeing that something I built could support other scientists in their own research made me especially proud and reminded me why I enjoy method development so much. In research, it is often easy to focus mainly on open questions, challenges, and details that still need improvement, which is why positive feedback from the scientific community can feel particularly meaningful. I also felt very honoured to receive a fellowship from the Bayer Foundation that supported my research stay in Boston, as well as being selected to attend the Lindau Nobel Laureate Meeting. Both experiences were very motivating and gave me additional confidence in my scientific path.

What is a “day in the life” of you like?

On a typical workday, I go to my office, which I share with three other people. Most of my time is spent working independently on my computer, doing programming, data analysis, and developing methods, as well as interpreting results and planning next steps. In parallel, there are several regular meetings that structure the week, such as group meetings, journal clubs, seminars, and discussions with my supervisor or collaborators. These exchanges are an important part of the work, as they often help refine ideas and put results into a broader context. I also enjoy the teaching side of academia. Occasionally, I contribute to courses for master’s students, either by preparing material, giving lectures, or supervising computational exercises. In addition, I often supervise bachelor’s or master’s students in their projects, which adds another very rewarding layer of day-to-day interaction.

What are you seeking to accomplish in your career?

What motivates me most in research is the possibility of translating large-scale biological and clinical data into insights that improve our understanding of disease and may eventually support better treatment strategies. To contribute to this goal, I am particularly interested in developing computational and statistical methods for analysing complex datasets, including single-cell perturbation data, and in applying these approaches to biologically and clinically relevant questions. In the future, I see myself working in highly interdisciplinary environments where computational method development is closely connected to biological and clinical questions, and where close collaboration across disciplines helps shape the research direction. In addition, I have really enjoyed supervising students and contributing to teaching, which is something I would like to continue developing further in the future.

What do you like to do when you’re not doing research?

Outside of research, I really enjoy being active and spending time outdoors. In the past two years, I have especially gotten into triathlon, which for me is a great balance of different sports and a good way to clear my mind after work. I also enjoy spending time with friends and family, for example through cooking evenings, board games, or cycling trips together. For quieter moments, I like listening to audiobooks, which is a nice way to relax and switch off at the end of the day.

What advice do you have for other women interested in science / in your discipline?

One piece of advice I would give is to try out different things early on and not be afraid of making mistakes along the way. In science especially, careers typically involve multiple steps, topics, and often also changes of location, which can feel intimidating at first. From my own experience, it is important to trust yourself enough to try these opportunities, even if you are unsure whether they are the right fit. If something turns out not to be the right direction, it is always possible to adjust course — but I think it is a pity to miss opportunities because of a lack of confidence in the beginning. I also found it very helpful to actively ask others for advice and seek out mentors when navigating decisions or new opportunities.

In your opinion, what will be the next great breakthrough in science / in your discipline?

I think one of the most exciting developments in computational biology will be the ability to build increasingly predictive models of cellular behaviour. A major long-term goal in the field is the idea of a “virtual cell” — computational models that can capture and predict how cells behave, interact, and transition between cellular states. While current models are still far from fully capturing the complexity of biology, progress in AI and the increasing availability of large-scale single-cell datasets are moving the field forward very quickly. In particular, I think these approaches will become extremely valuable for predicting cellular responses to genetic or chemical perturbations. This could help researchers identify promising experiments or therapeutic targets much more efficiently, especially in settings where experimentally testing all possible perturbation combinations would be impossible.

What should be done to increase the number of female scientists and professors?

While many biology-related fields now have a high proportion of female students and early-career researchers, this is often less reflected in computational subfields and senior academic positions. I think one important reason is that academic careers still involve a lot of uncertainty through short-term contracts and frequent expectations to relocate, which can make long-term personal or family planning difficult. Creating more stable and family-friendly career structures earlier on, for example through tenure-track programmes, flexible working models, or even shared leadership structures, could help make academic careers more sustainable. I also think visible role models are extremely important. Seeing researchers and professors who successfully combine scientific careers with family life can make academia feel much more accessible and realistic for the next generation.

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