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Some sources were due to missing reactions in the pathways.
The lack of accuracy at predicting some experiments could be explained by missing reactions in the model, especially regarding the transport of specific carbon sources.
Our focus was to assess whether SMILEY could generate biologically relevant hypotheses of missing reactions in human metabolism rather than produce a detailed list of missing enzyme functionalities.
Knowledge of coupled reactions enables finding equivalent knockouts and, when used in conjunction with directionality data, enables the identification of missing reactions in a reconstruction [ 4].
Genome-scale metabolic network models typically contain hundreds of reactions, and are typically missing reactions in their early formulations, since most genome-scale networks are derived from genome annotations that are themselves incomplete.
The gap filling process identified some missing reactions in the pathways that were added as non-gene associated reactions to enable the reconstructed network to synthesize metabolites for biomass formation.
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These missing reactions create gaps in the metabolic network that represent either real differences in physiology or incorrect absence calls due to methodological issues such as incorrect clustering, mis-annotation, or missing gene calls.
It is, therefore, likely that the general conclusions obtained with the iJO1366 model [ 19] are robust to missing reactions or errors in the reconstruction.
All these results suggest that the conclusions of this work are highly robust to missing reactions or errors in the reconstruction.
Our results can help to generate hypotheses on missing reactions and manifest differences in highly conserved pathways, which is useful for biology and life science applications.
Although very high percentages (66.6%) of the enzymes were associated with the reactions, there were missing reactions that were not represented in these databases.
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