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In the second step, enzyme information databases, namely ExPASy [ 52], BioCyc [ 53] and BRENDA [ 54] were explored to include the missing reactions to the model.
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The 18 false negatives we observed may be attributed to missing reactions, corresponding to Y. lipolytica genes that are still unannotated, or to gaps in understanding of redundancy in the network.
Some sources were due to missing reactions in the pathways.
Central carbon metabolism is highly reticulate, with many alternative metabolic routes for missing reactions [ 17], which may lead to fewer highly constrained reaction sets.
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.
The algorithms GapFind/GapFill [ 9] and GrowMatch [ 15] were later developed, and could predict missing reactions by connecting model gaps and by comparing model predictions to gene essentiality data, respectively.
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.
Other missing reactions were added to the model as non-gene associated reactions to enable the reconstructed network to synthesize metabolites for biomass formation.
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].
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.
Based on the results from metabolomic studies, missing reactions from the metabolic network could be inferred [ 3] and ChlamyCyc, a web portal for systems analyses, was generated [ 4].
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