Addressing uncertainty in genome-scale metabolic model reconstruction and analysis

Abstract

The reconstruction and analysis of genome-scale metabolic models constitutes a powerful systems biology approach, with applications ranging from basic understanding of genotype-phenotype mapping to solving biomedical and environmental problems. However, the biological insight obtained from these models is limited by multiple heterogeneous sources of uncertainty, which are often difficult to quantify. Here we review the major sources of uncertainty and survey existing approaches developed for representing and addressing them. A unified formal characterization of these uncertainties through probabilistic approaches and ensemble modeling will facilitate convergence towards consistent reconstruction pipelines, improved data integration algorithms, and more accurate assessment of predictive capacity.

Fig. 1

A general progression for genome-scale metabolic model reconstruction and analysis is represented by five major steps. The central black arrows demonstrate a standard approach, which yields a single output from each step. The gray arrows represent the uncertainty in this process, with the output of each step as an ensemble of possible results. The new additions to the model at each step are shown in red: circles represent metabolites, stars represent biomass components, arrows represent metabolic reactions, and bold arrows represent a specific flux distribution