%0 Journal Article %1 Dreyfuss2013Reconstruction %A Dreyfuss, Jonathan M. %A Zucker, Jeremy D. %A Hood, Heather M. %A Ocasio, Linda R. %A Sachs, Matthew S. %A Galagan, James E. %D 2013 %I Public Library of Science %J PLoS Comput Biol %K genome-scale metabolic-networks reconstruction %N 7 %P e1003126+ %R 10.1371/journal.pcbi.1003126 %T Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM %U http://dx.doi.org/10.1371/journal.pcbi.1003126 %V 9 %X Author SummaryFew organisms have been as foundational to the development of modern genetics and cellular metabolism as Neurospora crassa. Given the wealth of knowledge available for this filamentous fungus, the effort required to manually curate a high-quality genome-scale metabolic reconstruction would be daunting. To aid the reconstruction process, we developed three optimization-based algorithms. The first algorithm predicts flux while linearly accounting for metabolite dilution; the second algorithm removes blocked reactions with one compact linear program; and the third algorithm reconciles differences between in silico predictions and experimental observations of mutant viability. We have used these algorithms to develop the first genome-scale metabolic model for Neurospora. We have validated the accuracy of our model against an independent test set of more than 300 growth/no-growth phenotypes, and our model displays 93\% sensitivity and specificity. Simulating the biochemical genetics experiments originally performed on Neurospora, we comprehensively predicted essential genes, nutrient rescues of auxotroph mutants and synthetic lethal interactions. With these predictions, we provide potential mechanistic insight into known mutant phenotypes, and testable hypotheses for novel mutant phenotypes. The model, the algorithms and the testable hypotheses provide a computational foundation for the study of Neurospora crassa metabolism.

@article{Dreyfuss2013Reconstruction, abstract = {Author {SummaryFew} organisms have been as foundational to the development of modern genetics and cellular metabolism as Neurospora crassa. Given the wealth of knowledge available for this filamentous fungus, the effort required to manually curate a high-quality genome-scale metabolic reconstruction would be daunting. To aid the reconstruction process, we developed three optimization-based algorithms. The first algorithm predicts flux while linearly accounting for metabolite dilution; the second algorithm removes blocked reactions with one compact linear program; and the third algorithm reconciles differences between in silico predictions and experimental observations of mutant viability. We have used these algorithms to develop the first genome-scale metabolic model for Neurospora. We have validated the accuracy of our model against an independent test set of more than 300 growth/no-growth phenotypes, and our model displays 93\% sensitivity and specificity. Simulating the biochemical genetics experiments originally performed on Neurospora, we comprehensively predicted essential genes, nutrient rescues of auxotroph mutants and synthetic lethal interactions. With these predictions, we provide potential mechanistic insight into known mutant phenotypes, and testable hypotheses for novel mutant phenotypes. The model, the algorithms and the testable hypotheses provide a computational foundation for the study of Neurospora crassa metabolism.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Dreyfuss, Jonathan M. and Zucker, Jeremy D. and Hood, Heather M. and Ocasio, Linda R. and Sachs, Matthew S. and Galagan, James E.}, biburl = {https://www.bibsonomy.org/bibtex/2a6967420d44bb81f0dbcef5bddbd6082/karthikraman}, citeulike-article-id = {12515221}, citeulike-linkout-0 = {http://dx.doi.org/10.1371/journal.pcbi.1003126}, citeulike-linkout-1 = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3730674/}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/23935467}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=23935467}, day = 18, doi = {10.1371/journal.pcbi.1003126}, interhash = {d081736c7f2f57c4d9a45a5f8541bded}, intrahash = {a6967420d44bb81f0dbcef5bddbd6082}, issn = {1553-7358}, journal = {PLoS Comput Biol}, keywords = {genome-scale metabolic-networks reconstruction}, month = jul, number = 7, pages = {e1003126+}, pmcid = {PMC3730674}, pmid = {23935467}, posted-at = {2013-09-12 13:19:02}, priority = {2}, publisher = {Public Library of Science}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {Reconstruction and Validation of a {Genome-Scale} Metabolic Model for the Filamentous Fungus Neurospora crassa Using {FARM}}, url = {http://dx.doi.org/10.1371/journal.pcbi.1003126}, volume = 9, year = 2013 }