A Bayesian Approach to High-Throughput Biological Model Generation

Abstract. With the availability of hundreds and soon-to-be thousands of complete genomes, the construction of genome-scale metabolic models for these organisms has attracted much attention. However, manual work still dominates the process of model generation and leads to the huge gap between the number of complete genomes and genome-scale metabolic models. The challenge in constructing a genome-scale models from existing databases is that usually such a directly extracted model is incomplete and contains network holes. Network holes occur when a network is disconnected and certain metabolites cannot be produced or consumed. In order to construct a valid metabolic model, network holes need to be filled by introducing candidate reactions into the network. Toward the highthroughput generation of biological models, we propose a Bayesian approach to improving draft genome-scale metabolic models. A collection of 23 types of biological and topological evidence is extracted from databases the...

• Type: Conference
• Year: 2009
• Where: BICOB
• Authors: Xinghua Shi, Rick L. Stevens
• Keywords: BICOB 2009, Computational Biology, Genome-scale Metabolic Models, Individual Predictors, Network Holes