Integrating prior biological knowledge and graphical LASSO for network inference

Abstract

Systems biology aims at unravelling the mechanisms of complex diseases by investigating how individual elements of the cell (e.g., genes, proteins, metabolites, etc.) interact with each other. Network-based methods provide an intuitive framework to model, characterize, and understand these interactions. To reconstruct a biological network, one can either query public databases for known interactions (knowledge-driven approach) or build a mathematical model to measure the associations from data (data-driven approach). In this paper, we propose a new network inference method, integrating knowledge and data-driven approaches. The method integrates prior biological knowledge (i.e., protein-protein interactions from BioGRID database) and a Gaussian graphical model (i.e., graphical LASSO algorithm) to construct robust and biologically relevant network. The network is then utilized to extract differential sub-networks between case and control groups using the result from a statistical analysis (e.g., logistic regression). We applied the proposed method on a proteomic dataset acquired by analysis of sera from hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. The differential sub-networks led to the identification of hub proteins and key pathways, whose relevance to HCC study has been confirmed by literature survey.