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MEMOTE for standardized genome-scale metabolic model testing

Lieven, Christian; Beber, Moritz E.; Olivier, Brett G.; Bergmann, Frank T.; Ataman, Meric; Babaei, Parizad; Bartell, Jennifer A.; Blank, Lars M.

Reconstructing metabolic reaction networks enables the development of testable hypotheses of an organisms metabolism under different conditions1. State-of-the-art genome-scale metabolic models (GEMs) can include thousands of metabolites and reactions that are assigned to subcellular locations. Geneproteinreaction (GPR) rules and annotations using database information can add meta-information to GEMs. GEMs with ...


Publisher Correction: MEMOTE for standardized genome-scale metabolic model testing

Lieven, Christian; Beber, Moritz E.; Olivier, Brett G.; Bergmann, Frank T.; Ataman, Meric; Babaei, Parizad; Bartell, Jennifer A.; Blank, Lars M.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.


Automated reconstruction and comparison of metabolic models for diverse fungal ...

Edirisinghe, Janaka N.; Faria, José P.; Liu, Filipe Alexandre Wang; Henry, Christopher S.


Improving automated model reconstruction across phylogenetically diverse genome...

Faria, José P.; Edirisinghe, Janaka N.; Seaver, Samuel M. D.; Liu, Filipe Alexandre Wang; Weisenhorn, Pamela; Jeffryes, James G.; Gu, Tian; Zhang, Qizh


Automated pathway curation and improving metabolic model reconstruction based o...

Edirisinghe, Janaka N.; Faria, J.; Liu, Filipe Alexandre Wang; Xavier, Joana Rute Calça; Seaver, Samuel; Weisenhorn, Pamela; Jeffryes, James; Gu, Tian

Metabolic models generated by automated reconstruction pipelines are widely used for high-throughput prediction of microbial phenotypes. However, the generation of accurate in-silico phenotype predictions based solely on genomic data continues to be a challenge as metabolic models often require extensive gapfilling in order to produce biomass. As a result, the true physiological profile of an organism can be al...


Computing and applying atomic regulons to understand gene expression and regula...

Faria, J.; Davis, James J.; Edirisinghe, Janaka N.; Taylor, Ronald C.; Weisenhorn, Pamela; Olson, Robert D.; Stevens, Rick L.; Rocha, Miguel

Understanding gene function and regulation is essential for the interpretation prediction and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets Atomic Regulons ARs represent fundamental units of function within a cell and...


Enabling comparative modeling of closely related genomes: Example genus Brucella

Faria, José P.; Edirisinghe, Janaka N.; Davis, James J.; Disz, Terrence; Hausmann, Anna; Henry, Christopher S.; Olson, Robert; Overbeek, Ross A.

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this short report, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of met...


Tapping the wealth of microbial data in high-throughput metabolic model reconst...

Colasanti, Ric; Edirisinghe, Janaka N.; Khazaei, Tahmineh; Faria, J.; Seaver, Sam; Xia, Fangfang; Henry, Christopher

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 modifica...


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