Tapping the wealth of microbial data in high-throughput metabolic model reconstruction
| Autor: | Colasanti, Ric, Edirisinghe, Janaka N., Khazaei, Tahmineh, Faria, J., Seaver, Sam, Xia, Fangfang, Henry, Christopher |
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| Přispěvatelé: | Universidade do Minho |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
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| Popis: | Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail. We acknowledge the entire SEED, Model SEED, and CytoSEED teams at Argonne National Laboratory, Fellowship for Interpretation of Genomes, Hope College and University of Chicago for efforts on the frameworks described in this chapter. This work was supported by the US Department of Energy under contract DE-ACO206CH11357 (J.F., F.X., C.H.), and the National Science Foundation under grants PGRP-1025398 (S.S.), EFRI-1137089 (R.C., T.K.) and MCB-1153413 (J.E.). José P Faria acknowledges funding from his Ph.D. grant SFRH/BD/70824/2010 of the FCT (Portuguese Foundation for Science and Technology). |
| Databáze: | OpenAIRE |
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