Using a logical model to predict the growth of yeast

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dc.contributor.author King, Ross Donald
dc.contributor.author Whelan, Ken
dc.date.accessioned 2008-07-18T08:16:46Z
dc.date.available 2008-07-18T08:16:46Z
dc.date.issued 2008-02-12
dc.identifier.citation King , R D & Whelan , K 2008 , ' Using a logical model to predict the growth of yeast ' BMC Bioinformatics , vol 9 , no. 97 . en
dc.identifier.issn 1471-2105
dc.identifier.other PURE: 77170
dc.identifier.other dspace: 2160/605
dc.identifier.uri http://hdl.handle.net/2160/605
dc.description Whelan, K. E. and King, R. D. Using a logical model to predict the growth of yeast. BMC Bioinformatics 2008, 9:97 en
dc.description.abstract Background: A logical model of the known metabolic processes in S. cerevisiae was constructed from iFF708, an existing Flux Balance Analysis (FBA) model, and augmented with information from the KEGG online pathway database. The use of predicate logic as the knowledge representation for modelling enables an explicit representation of the structure of the metabolic network, and enables logical inference techniques to be used for model identification/improvement. Results: Compared to the FBA model, the logical model has information on an additional 263 putative genes and 247 additional reactions. The correctness of this model was evaluated by comparison with iND750 (an updated FBA model closely related to iFF708) by evaluating the performance of both models on predicting empirical minimal medium growth data/essential gene listings. Conclusion: ROC analysis and other statistical studies revealed that use of the simpler logical form and larger coverage results in no significant degradation of performance compared to iND750. en
dc.language.iso eng
dc.relation.ispartof BMC Bioinformatics en
dc.title Using a logical model to predict the growth of yeast en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1186/1471-2105-9-97
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Computational Biology and Bioinformatics en
dc.description.status Peer reviewed en


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