Signaling Pathway Reconstruction Analysis Streamliner

Abstract

Signaling pathways transmit and process signals into cellular responses within cells. Understanding these pathways is essential for uncovering the mechanisms underlying different biological processes. Curated databases provide standardized representations of known signaling pathways; however, they lack context specific details needed for answering specific biological questions. Pathway reconstruction algorithms address this gap by mapping experimental omics data onto known interactomes to generate condition specific subnetworks. These subnetworks can highlight active signaling molecules and novel interactions, guiding further research into different biological processes. However, using pathway reconstruction algorithms remain a challenge. To address this, I led the development of the Signaling Pathway Reconstruction Analysis Streamliner (SPRAS), a computational framework that standardizes and automates the reconstruction process. SPRAS provides a modular framework for data preprocessing, algorithm execution, result postprocessing, and downstream analysis, enabling scalable pathway reconstruction analyses.