Construction and Evaluation of Celltype-Specific Protein-Protein Interaction Networks

Abstract

Protein-protein interaction (PPI) networks serve as critical tools for probing the molecular mechanisms that define function and connect genotype to phenotype, yet context-agnostic PPI databases fail to capture the celltype-specific contexts in which these interactions occur. This thesis addresses this limitation by integrating single-cell RNA sequencing (scRNA-seq) data into these context-agnostic human PPI networks using two dominant methods in the literature: SCINET (parametric) and PINN (non-parametric). Using a dataset of dopaminergic midbrain neurons implicated in Parkinson's disease, we construct networks with these methods and offer ways to evaluate these networks to ensure they preserve important properties at multiple scales of biology. These include evaluations at the level of functional protein complexes, pathways, celltype-specific processes, and systematic interactions within tissues. Our analyses show that genes implicated in Parkinson's Disease play a significant role in the topology of their respective networks, highlighting the essentiality of these proteins. Furthermore, we construct contextual embeddings using PINNACLE, a graph neural network model for single-cell biology, to represent proteins at a systems-level scale. Despite limitations inherent to PPI representations of biological processes, this thesis emphasizes the importance of context-specificity in these networks, compares different methods of their construction, and offers a robust system of evaluations that show the strengths of different construction methods at various dimensions of biology.