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Research focus

Our research is centered on understanding how cells maintain protein homeostasis (proteostasis), particularly under stress and during aging.

To tackle this, we combine genome-wide screening in model systems with automated experimental workflows and data-driven analysis. By integrating large-scale experiments with bioinformatics—and increasingly AI-assisted approaches—we can systematically explore how proteostasis is regulated and how it fails over time.

This allows us to move beyond individual factors and instead build a more integrated, systems-level view of cellular function, with clear relevance for human health and disease.

Our approach

High-throughput functional genomics
A central part of our work is genome-scale screening in yeast. Using barcoded libraries and automated platforms, we can interrogate thousands of genetic perturbations in parallel. This gives us the ability to explore complex biological processes in a systematic and unbiased way.

Data-driven biology and bioinformatics
We combine these large datasets with computational analysis and predictive approaches to extract mechanistic insight. Our work sits deliberately at the intersection of hypothesis-driven and data-driven research, where large-scale discovery feeds back into focused mechanistic questions.

AI-assisted data pipelines
To keep up with the scale and complexity of our data, we develop and use automated and AI-assisted analysis pipelines. These help us process data more efficiently and reproducibly, and open the door to more predictive and quantitative ways of understanding biology.

Scalable and reproducible workflows
An important goal of the lab is to establish robust and reusable analysis workflows. We aim to generate approaches that are not only useful for our own projects, but can be applied more broadly across different datasets and questions.

Research areas

Proteostasis and aging
Proteostasis is a dynamic balance of protein synthesis, folding, and degradation that is essential for cellular function. With age, this balance becomes increasingly difficult to maintain, leading to the accumulation of damaged and misfolded proteins.

We use genome-wide screening combined with computational analysis to identify key components and regulatory layers of the proteostasis network, and to understand how its capacity changes over time.

From model systems to human biology
We take advantage of the scalability and experimental power of yeast, and complement this with validation in more complex systems. This allows us to identify conserved principles and translate our findings into biologically meaningful contexts.

Infection biology and screening applications
We are also expanding our screening approaches to new areas, including infection biology, where systematic and high-throughput strategies can help uncover new host–pathogen interactions and potential intervention points.

Vision

By combining high-throughput biology with computational and AI-driven approaches, we want to contribute to a broader shift toward data-driven life science, while staying grounded in clear biological questions.

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Publications