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Ahmed Youssef Successfully Defends

Congratulations to Ahmed Youssef on the successful defense of their thesis. "Computational Methods to Uncover Cell State Proteomes and Profile Protein Interaction Dynamics" Great job Ahmed! 

To learn more about Ahmed's thesis, read their abstract below:



Proteins, through their networks of interactions, carry out most essential biological processes governing cellular functions, yet the proteome remains largely unexplored at single-cell resolution and existing models of protein interactions do not capture the dynamic nature of the interactome, representing crucial gaps in our understanding of proteome organization and function. Despite the critical position that the proteome occupies in the functional landscape of the cell, proteomics has lagged behind other data-driven systems biology subfields when it comes to the development of tailored computational strategies for interrogating its complexities. The research projects discussed in this dissertation aim to build upon the rapidly evolving computational proteomics toolkit by developing novel algorithms to address existing analysis gaps with regards to single-cell proteomics and protein interaction dynamics.


In this dissertation, we present DESP, a novel algorithm that leverages independent readouts of cellular proportions, such as those from single-cell RNA-sequencing, to resolve the relative contributions of cell states to bulk molecular measurements, most notably quantitative proteomics, recorded in parallel. DESP provides a generalizable computational framework for modeling the relationship between bulk and single-cell molecular measurements, enabling the study of proteomes and other molecular profiles at the cell state-level using established bulk-level workflows.We applied DESP to an in-vitro model of the epithelial-to-mesenchymal transition and demonstrated its ability to accurately reconstruct cell state signatures from bulk-level measurements of both the proteome and transcriptome while providing insights into transient regulatory mechanisms. 


This dissertation also describes the development of a novel analysis pipeline for modeling protein interaction remodeling from dynamic CF/MS data. Protein interactions can be disrupted by many triggers, such as pathogen infection or mutations in protein-coding genes, yet most studies in the field focused on characterizing the interactome in a static manner, with few devoted to investigating the dynamic nature of these interactions. As an application of our pipeline, we profiled the dynamics of the Escherichia coli interactome in response to changes in its growth environment. Our results shed light on the mechanisms governing protein interaction remodeling, while also providing a rigorous analytical framework for quantifying interaction dynamics on an interactome-wide scale, representing an important step towards accurate modeling of dynamic biological systems.

Major Professors: Andrew Emili & Mark Crovella


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