Genome-Scale Metabolic Network Models of Bacillus Species Suggest that Model Improvement is Necessary for Biotechnological Applications

A genome-scale metabolic network model (GEM) is a mathematical representation of an organism's metabolism. Today, GEMs are popular tools for computationally simulating the biotechnological processes and for predicting biochemical properties of (engineered) strains. In the present study, we have evaluated the predictive power of two GEMs, namely Bsu1103 (for 168) and MZ1055 (for WSH002). For comparing the predictive power of and GEMs, experimental data were obtained from previous wet-lab studies included in PubMed. By using these data, we set the environmental, stoichiometric and thermodynamic constraints on the models, and FBA is performed to predict the biomass production rate, and the values of other fluxes. For simulating experimental conditions in this study, COBRA toolbox was used. By using the wealth of data in the literature, we evaluated the accuracy of simulations of these GEMs. Our results suggest that there are some errors in these two models which make them unreliable for predicting the biochemical capabilities of these species. The inconsistencies between experimental and computational data are even greater where and do not have similar phenotypes. Our analysis suggests that literature-based improvement of genome-scale metabolic network models of the two species is essential if these models are to be successfully applied in biotechnology and metabolic engineering.