Abstract
BackgroundSeveral methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation. Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Recently, mixed integer programming has been used to encode these gene-protein-reaction (GPR) relationships into a single optimization problem, but these techniques are often of limited generality and lack a tool for automating the conversion of rules to a coupled regulatory/metabolic model.ResultsWe present TIGER, a Toolbox for Integrating Genome-scale Metabolism, Expression, and Regulation. TIGER converts a series of generalized, Boolean or multilevel rules into a set of mixed integer inequalities. The package also includes implementations of existing algorithms to integrate high-throughput expression data with genome-scale models of metabolism and transcriptional regulation. We demonstrate how TIGER automates the coupling of a genome-scale metabolic model with GPR logic and models of transcriptional regulation, thereby serving as a platform for algorithm development and large-scale metabolic analysis. Additionally, we demonstrate how TIGER's algorithms can be used to identify inconsistencies and improve existing models of transcriptional regulation with examples from the reconstructed transcriptional regulatory network of Saccharomyces cerevisiae.ConclusionThe TIGER package provides a consistent platform for algorithm development and extending existing genome-scale metabolic models with regulatory networks and high-throughput data.
Highlights
Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation
Constraint-Based Reconstruction and Analysis (COBRA) methods have allowed the study of metabolism on a genome-wide scale [1]
Predicting the metabolic capabilities of a COBRA model is possible with Flux Balance Analysis (FBA), a two-stage mathematical technique based on the observation that metabolic networks often display optimal dynamics [7]
Summary
Several methods have been developed for analyzing genome-scale models of metabolism and transcriptional regulation Many of these methods, such as Flux Balance Analysis, use constrained optimization to predict relationships between metabolic flux and the genes that encode and regulate enzyme activity. Constraint-Based Reconstruction and Analysis (COBRA) methods have allowed the study of metabolism on a genome-wide scale [1]. These models have been used to understand the interplay between environmental and genetic perturbations and the metabolic capabilities of an organism. Predicting the metabolic capabilities of a COBRA model is possible with Flux Balance Analysis (FBA), a two-stage mathematical technique based on the observation that metabolic networks often display optimal dynamics [7]. By assuming that the fluxes through a metabolic network have evolved to maximize an objective, FBA eliminates the need for detailed kinetic information for each of the thousands of reactions in a complete metabolic reconstruction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
