Modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis: how good are genome-scale metabolic networks at predicting the differences?

To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

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

BACKGROUND: 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. OBJECTIVES: In the present study, we have evaluated the predictive power of two GEMs, namely iBsu1103 (for Bacillus subtilis 168) and iMZ1055 (for Bacillus megaterium WSH002). MATERIALS AND METHODS: For comparing the predictive power of Bacillus subtilis and Bacillus megaterium 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. RESULTS: By using the wealth of data in the literature, we evaluated the accuracy of in silico 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 B. subtilis and B. megaterium do not have similar phenotypes. CONCLUSIONS: Our analysis suggests that literature-based improvement of genome-scale metabolic network models of the two Bacillus species is essential if these models are to be successfully applied in biotechnology and metabolic engineering.