Research: Bridging the Scales of Life

For a detailed record of my academic history including publication and funding list, please see also my [Curriculum Vitae (PDF)] or visit my [Google Scholar Profile].


A unifying theme of my research is bridging different scales of biological organization from gene regulation to cellular, population and evolutionary dynamics. I combine mechanistic modeling with high-resolution data to construct a multi-scale framework for understanding how life adapts and persists.


Current & Future Directions

Flagellar Gene Networks in Salmonella

Currently, at Humboldt-Universität zu Berlin in Molecular Microbiology Group (AG Erhardt), I am using bacterial flagellar gene regulation as a model system for quantitative traits. We study how stochastic gene networks generate heterogeneity in flagella numbers among identical cells. This approach allows us to construct realistic genotype–phenotype–fitness maps for bacteria, where we aim to better understand how adaptation proceeds under selection—with direct implications for pathogenicity, the evolution of resistance, and network rewiring.

Evolution in Structured Populations & Resistance

Building on my previous work, I am extending my single-cell fitness models capturing non-genetic heterogeneity to predict population and evolutionary dynamics for ageing or stressed populations. This work is critical for understanding persistence dynamics and the evolution of antibiotic resistance. By integrating physiology-based models with modern population-genetic theory, I aim to predict how "damaged" or "aged" sub-populations contribute to long-term evolutionary survival.


Previous Research & Expertise

Single-Cell Stochasticity & Quantitative Microbiology

Working with E. coli and microfluidic "mother machine" data at Freie Universität in Berlin (Evolutionary Demography), I investigated how stochastic processes like damage accumulation and division asymmetry shape individual fitness. My research provides a quantitative link between cellular ageing and population-level demographic patterns and fitness, highlighting the necessity of age- and stage-structured models in bacterial evolution.


Biophysical–Evolutionary Modeling

During my PhD at IST Austria (in Nick Barton's Mathematical Evolutionary Genetics Group, coadvised by biophysicist Gasper Tkacik), I bridged population genetics and biophysics to develop a quantitative theory of Transcription Factor (TF) binding site evolution. By modeling mutation, selection, and drift, I established mechanistic turnover rates for binding sites. This work created a vital link between molecular biophysics and population-genetic predictions.

Gene Regulatory Network (GRN) Modeling

My foundational work focused on the regulatory topology of Saccharomyces cerevisiae (during my master study at Koç University with Alkan Kabakçıoğlu). I developed models demonstrating how network architecture and feedback structures govern system robustness and dynamical behavior. This work provided the theoretical background in systems biology that informs my current understanding of how regulatory dynamics work.