(191do) Improving Automated Model Reconstruction Across Phylogenetically Diverse Genome-Scale Metabolic Models

Authors: 
Faria, J. P., Argonne National Laboratory
Edirisinghe, J. N., Argonne National Laboratory
Liu, F., Argonne National Laboratory
Seaver, S. M. D., Argonne National Laboratory
Weisenhorn, P., Argonne National Laboratory
Jeffryes, J. G., Argonne National Laboratory
Henry, C. S., Argonne National Laboratory
The Department of Energy Systems Biology Knowledgebase (KBase) is a platform designed to solve the grand challenges of Systems Biology. KBase has implemented bioinformatics tools that allow for multiple workflows including genome annotation, comparative genomics, and metabolic modeling. KBase now also includes a comprehensive database of over 80K reference genomes (approximately 5K complete genomes) from NCBI’s RefSeq. We have selected a phylogenetically diverse set of approximately 1000 genomes and built draft genome-scale metabolic models constructed using the ModelSEED pipeline implemented in KBase. In constructing these models, we were interested in improving the gene functional role – reaction templates and accuracy of biomass compositions used by ModelSEED when producing a draft model. We have curated the existing ModelSEED templates by both fixing and introducing new gene – reaction mappings. Previous ModelSEED biomass compositions often excluded metabolites that are essential for the viability of many organisms, while simultaneously including some non-essential metabolites. These errors in the biomass compositions lead to errors in the growth conditions and gene knockout phenotypes predicted by the models. To correct these problems, we conducted a sensitivity analysis on our full set of models, using a biomass composition that includes all possible essential biomass precursors. We also improved the ModelSEED gap filling database with new rules to restrict the addition of non-biological significant reactions. We then used this data to improve all of our models, validating our improved models with a diverse set of growth and knockout phenotype data. Our improved models are now available for download, viewing, and comparison in KBase.
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