Modeling and Analysis of Heterogeneous Regulation in Biological Networks

Abstract. In this study we propose a novel model for the representation of biological networks and provide algorithms for learning model parameters from experimental data. Our approach is to build an initial model based on extant biological knowledge, and refine it to increase the consistency between model predictions and experimental data. Our model encompasses networks which contain heterogeneous biological entities (mRNA, proteins, metabolites) and aims to capture diverse regulatory circuitry on several levels (metabolism, transcription, translation, post-translation and feedback loops among them). Algorithmically, the study raises two basic questions: How to use the model for predictions and inference of hidden variables states, and how to extend and rectify model components. We show that these problems are hard in the biologically relevant case where the network contains cycles. We provide a prediction methodology in the presence of cycles and a polynomial time, constant factor ap...

• Type: Conference
• Year: 2004
• Where: RECOMB
• Authors: Irit Gat-Viks, Amos Tanay, Ron Shamir
• Keywords: Computational Biology, Constant Factor Approximation, Extensive Literature Survey, Lysine Biosynthesis Pathway, RECOMB 2004