Research Projects

My research focuses on understanding the regulation of dynamic biological systems by combining quantitative proteomics, computational analysis, and experimental biology. Below are selected research projects spanning my PhD and postdoctoral work.

Postdoctoral Research

Temporal regulation of the Anaphase-Promoting Complex/Cyclosome (APC/C)

Biological question
How is APC/C activity temporally controlled during mitosis, and how does progressive phosphorylation contribute to cell cycle timing?

Approach
This project integrates:

• Quantitative mass spectrometry (DIA)
• Structural mass spectrometry crosslinking strategies
• Xenopus laevis egg extracts as a cell-cycle model system

Key findings

• Progressive phosphorylation of APC/C components acts as a mitotic timing mechanism
• Identification of phosphorylation patterns correlated with APC/C activation states
• Quantitative mapping of regulatory interactions during mitotic progression

Tools & technologies MaxQuant · PEAKS · DIA-NN · Custom Python/C++ pipelines ·

Doctoral Research (PhD)

Quantification of proteome variations at the intact protein level

Research objective
To develop robust experimental and computational strategies for quantifying proteome changes using a new isotope labeling approaches.

Methods developed

• Isotope-based metabolic labeling (SLIM / bSLIM)
• Signal processing and statistical analysis of quantitative MS data
• Integration of bottom-up and top-down proteomics workflows

Key outcomes

• Development of open-source algorithms for quantitative proteomics
• Application to pathogenic yeast Candida albicans
• Author of three first-author peer-reviewed publications

Scientific impact This work provided new computational frameworks to assess quantitative confidence in proteomics experiments and enabled deeper interpretation of proteome variability.

Ongoing & Future Directions

• Dynamic modeling of post-translational modifications
• Systems-level integration of proteomics and cell cycle data
• Reproducible computational pipelines for large-scale MS datasets