Advancing Epigenetics with Data Science: An Interview with Prof Alena van Bömmel

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In this interview, Prof Alena van Bömmel talks about her research at the intersection of epigenetics, computational biology and statistical modelling, and reflects on the challenges of the field, where meaningful biological signals must be extracted from large, often noisy datasets.

Prof van Bömmel, welcome to the BMC. Could you provide us with a brief overview of your research focus?

My research focus lies at the intersection of epigenetics, computational biology and statistical modelling, focusing on the complex roles of epigenetic changes in human aging and cancer. We develop machine learning and statistical models to uncover key sigantures in high dimensional epigenetic data, including DNA methylation, histone modifications, and open chromatin.

In the aging field, we constructed a novel, interpretable epigenetic clock and described nonlinear DNA methylation dynamics during aging.

Together with my collaborators, we developed an algorithm to classify brain tumors based on DNA methylation during the brain operation.

What aspects of your research are you particularly fascinated by?

What drives my work is a fascination with the hidden mathematical logic that governs biology. While biology often looks disorganised, I find it incredible that we can use rigorous statistical frameworks to decode its most complex layers, such as epigenetics. In this context, I am especially fascinated by the multilevel epigentic mechanisms that mediate gene expression - mechanisms that are still not fully understood. In my specific research field, I am particularly excited how universal statistical models can address diverse questions in molecular biology, from enhancer activity modelling to age prediction across tissues. This demonstrates the power of computational and statistical approaches in biology. And waiting for the computational results is always thrilling – will they confirm expected features or uncover unexpected biological insights?

Which methods will your team use to explore these mechanisms?

We primarily analyze bulk epigenetic and transcriptomic sequencing data, including whole genome bisulfite sequencing for measuring DNA methylation, ChIP-seq or Cut&Tag for histone modifications and ATAC-seq for open chromatin assessment. Additionally, we are increasingly leveraging single-cell multi-omics data to dissect regulatory dynamics in aging, neurodegeneration and cancer.

What encouraged you to join the BMC?

I was inspired by the remarkable interdisciplinary ecosystem of the BMC - spanning biochemistry, genomics, epigenetics, proteomics, neurobiology and clinical applications - areas that perfectly complement my work in epigenetic modeling. Also the stimulating and collaborative atmosphere in the Biochemistry Department was really contagious.

What do you think are the biggest challenges in your research field?

In computational biology, the biggest challenges stem from small sample sizes, high variability and usually large number of missing values complemented by unknown confounders like batch effects. On the other hand, we are able to measure hundreds of thousands of potential regulatory features. Extracting the true biological signal from large noisy data requires sophisticated statistical modelling and proper experimental setup to distinguish meaningful patterns. Very important role play also publicly available data collections that help us to train and validate our models.

You are currently recruiting for your research group. What can new team members expect when joining your group at the BMC?

My new team members can look forward to inclusive, non-hierarchical group dynamics emphasizing respectful communication, lively scientific discussions and equal contributions from everyone. I prioritize the person behind the scientist and try to actively listen to students’ challenges, struggles, and concerns while offering personalized mentorship to support both professional growth and personal well-being.