Experimental & Computational Methods

My work is centered on methodological rigor, reproducibility, and the integration of experimental and computational approaches. Below is an overview of the key methods and pipelines I routinely use.

Quantitative Proteomics

Mass Spectrometry

Software

Isotope Labeling Strategies

Schematic overview of the bSLIM metabolic labeling strategy used for quantitative proteomics
Overview of the bSLIM (bottom-up Simple Light Isotope Metabolic) labeling strategy. The schematic illustrates isotope incorporation, sample mixing, MS acquisition, and quantitative interpretation.

The bSLIM strategy enables robust quantitative proteomics by leveraging partial isotopic labeling. Unlike complete labeling approaches, bSLIM preserves biological flexibility while allowing accurate estimation of protein abundance changes across conditions.

Computational & Bioinformatics Analysis

Programming & Data Analysis

Focus areas

Exeample : Molar fraction estimation in bSLIM labeling

To quantify the fraction of non-labelled peptides in bSLIM experiments, I am using a mathematical formulation that relates isotopic incorporation to observed MS signal intensities.

Formula used to compute the molar fraction of non-labelled peptides in bSLIM experiments
Figure — Mathematical formulation used to estimate the molar fraction of non-labelled peptides (α) in bSLIM-based quantitative proteomics.

Cell Biology & Biochemistry