A system identification based framework for metabolic network analysis and its application to genome scale models of Scheffersomyces stipitis

Systems biology is a rapidly evolving discipline that seeks to determine how complex biological systems function by integrating experimentally derived information through mathematical modeling.  Genome-scale metabolic network models represent the link between the genotype and phenotype of the organism, where they are usually reconstructed based on the genome sequence annotation and relevant biochemical and physiological information. These models provide a holistic view of the organism’s metabolism, and constraint-based metabolic flux analysis methods have been used extensively to study genome-wide cellular metabolic networks. 

For any model based applications, the quality of the metabolic network model determines the outcome of the application. Therefore, it is critically important to determine how accurate a genome scale model is in describing the cellular metabolism of a given strain. Due to the complexity involved in a genome-scale model, a good agreement between measured and computed substrate pickup rates and product secretion rates does not necessarily mean the model is accurate. To address this challenge, in this work we present a novel system identification based framework to extract the qualitative, biological knowledge embedded in the quantitative simulation results from the metabolic network models. This framework bridges the gap between the complicated numerical results generated from different genome-scale models and the knowledge that can be easily understood by biologists. The effectiveness of the proposed framework is demonstrated by analyzing two published genome-scale models of Scheffersomyces stipitis, pin-pointing their limitations, as well as guiding the development of a modified genome scale model.