Mining and dynamic simulation of sub-networks from large biomolecular networks

Abstract

Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the computer simulation is an essential tool to understand biomolecular network models. This paper presents a novel method to mine, model and evaluate the regulatory system (a typical biomolecular network) which executes a cellular function. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the biomolecular network to obtain various sub-networks. Second, a computational model is generated for the sub-network and simulated to predict their behavior in the cellular context. We discuss and evaluate three advanced computational models: state-space model, probabilistic Boolean network model, and fuzzy logic model. Experimental results on time-series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large biomolecular network.