Systems biology of tumour evolution: estimating partial orders from omics data and entropy based stage identification

Abstract

This work presents analytical frameworks for exploring molecular dynamics and progressions in biological systems through the estimation of signalling entropy and ordinal classification. Signalling entropy is employed to quantify the complexity and adaptability of cellular signalling by integrating expression data with protein-protein interaction networks, and taking into consideration network topology and correction strategies that affect the reliability of entropy. Ordinal classification methods, on the other hand, are designed to detect ordered relationships within high-dimensional molecular data that depict sequential biological processes, such as development and disease evolution. The introduced novel technique captures the inherent ordering while reducing dimensionality, and enables the identification of alternative progression routes. Combined, these approaches enhance the robustness and interpretability of molecular analyses, contributing to a deeper understanding of cellular behaviour and disease mechanisms.