(547b) Large-Scale Reconstruction and Analysis of High Quality Core Metabolic Models for Over 8000 Microbial Genomes Conference: AIChE Annual MeetingYear: 2013Proceeding: 2013 AIChE Annual MeetingGroup: Topical Conference: Systems BiologySession: In Silico Systems Biology: Cellular and Organismal Models II Time: Wednesday, November 6, 2013 - 3:33pm-3:51pm Authors: Edirisinghe, J. N., Argonne National Laboratory Conrad, N., Argonne National Laboratory Frybarger, P. Shabbeer, A. Henry, C. S., Argonne National Laboratory Hundreds of microbial genomes are now being sequenced every day, demanding the analysis of a wide variety of microbes that may be utilized in multiple medical and industrial applications. Metabolic models are becoming widely popular for the high-throughput prediction of microbial phenotypes and behaviour. The Model SEED framework (Henry, DeJongh et al. 2010) provides a means for automatically constructing genome-scale metabolic models using high quality annotations from SEED subsystems (Aziz, Bartels et al. 2008). One of the common problems identified in draft models generated by the Model SEED is a lack in accuracy of pathways predicted for electron transport and ATP production. Central carbon pathways, electron transport chain, and fermentation pathways play an essential role in accurate metabolic modelling, and these pathways depend on environmental factors such as carbon source/electron donor, presence of oxygen or other anaerobic electron acceptors and the fermentation capability of the organism. Unlike eukaryotic organisms, bacteria electron Transport Chain (ETC) are highly diverse and complex, being categorized into obligate aerobic, obligate anaerobic, photosynthetic and facultative. As a result, the terminal electron acceptors range from organic compounds such as dimethyl sulfoxide (DMSO) to endless list of inorganic compounds such as nitrate, nitrite, sulphate, iron, etc. To overcome this problem, we developed tools to build high-quality core models based on well-studied, phylogenetically diverse model organisms, including Escherichia coli, Bacillus subtilis, Pseudomonas aeroginosa, Clostridium acetobutalicum and Paracococcus denitrificans. High quality core models may then be integrated into genome scale metabolic models, significantly improving the accuracy of energy metabolism pathways. We applied these tools to produce core metabolic models for nearly 8000 genomes, designed to be accurate depictions of central carbon metabolism, ETC, and energy production for microbial organisms. Building and analysing core models on almost every single sequenced bacterium helps to understand their metabolism on diverse environments and the metabolic variation in relationship to the phylogenetic distances. The ability of these models to correctly analyse electron transport and fermentation diversity of bacteria in various environmental conditions is critical for many industrial applications such as bioenergy production experiments. Simulating gene knockout experiments to optimize desired fermentation products such as ethanol or butanol, can be performed on a large variety of bacteria for industrial use before starting bench experiments. We also compare these models to explore the variability of these pathways that exist in microbial life, and by analysing the ability of our core models to synthesize ATP in the known growth conditions, we assess the extent to which functional fermentation pathways or electron transport chains are known for the organisms. In this study, we identify gaps in our knowledge of these pathways. Additionally, all tools developed for reconstruction, visualization, and analysis of core models were built into the DOE Systems Biology Knowledgebase (http://kbase.science.energy.gov), where they are available for use by the scientific community. We will highlight these tools in the context of our core model analysis.