Identifying functional families of trajectories in biological pathways by soft clustering: Application to TGF-βsignaling

Abstract

The study of complex biological processes requires to forgo simplified models for extensive ones. Yet, these models’ size and complexity place them beyond understanding. Their analysis requires new methods for identifying general patterns. The Transforming Growth Factor TGF-βis a multifunctional cytokine that regulates mammalian cell development, differentiation, and homeostasis. Depending on the context, it can play the antagonistic roles of growth inhibitor or of tumor promoter. Its context-dependent pleiotropic nature is associated with complex signaling pathways. The most comprehensive model of TGF-β-dependent signaling is composed of 15,934 chains of reactions (trajectories) linking TGF-βto at least one of its 159 target genes. Identifying functional patterns in such a network requires new automated methods. This article presents a framework for identifying groups of similar trajectories composed of the same molecules using an exhaustive and without prior assumptions approach. First, the trajectories were clustered using the Relevant Set Correlation model, a shared nearest-neighbors clustering method. Five groups of trajectories were identified. Second, for each cluster the over-represented molecules were determined by scoring the frequency of each molecule implicated in trajectories. Third, Gene set enrichment analysis on the clusters of trajectories revealed some specific TGF-β-dependent biological processes, with different clusters associated to the antagonists roles of TGF-β. This confirms that our approach yields biologically-relevant results. We developed a web interface that facilitates graph visualization and analysis. Our clustering-based method is suitable for identifying families of functionally-similar trajectories in the TGF-βsignaling network. It can be generalized to explore any large-scale biological pathways.