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An integrated approach to characterize genetic interaction networks in yeast metabolism

Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure...

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Autores principales: Szappanos, Balázs, Kovács, Károly, Szamecz, Béla, Honti, Frantisek, Costanzo, Michael, Baryshnikova, Anastasia, Gelius-Dietrich, Gabriel, Lercher, Martin J., Jelasity, Márk, Myers, Chad L., Andrews, Brenda J., Boone, Charles, Oliver, Stephen G., Pál, Csaba, Papp, Balázs
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125439/
https://www.ncbi.nlm.nih.gov/pubmed/21623372
http://dx.doi.org/10.1038/ng.846
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author Szappanos, Balázs
Kovács, Károly
Szamecz, Béla
Honti, Frantisek
Costanzo, Michael
Baryshnikova, Anastasia
Gelius-Dietrich, Gabriel
Lercher, Martin J.
Jelasity, Márk
Myers, Chad L.
Andrews, Brenda J.
Boone, Charles
Oliver, Stephen G.
Pál, Csaba
Papp, Balázs
author_facet Szappanos, Balázs
Kovács, Károly
Szamecz, Béla
Honti, Frantisek
Costanzo, Michael
Baryshnikova, Anastasia
Gelius-Dietrich, Gabriel
Lercher, Martin J.
Jelasity, Márk
Myers, Chad L.
Andrews, Brenda J.
Boone, Charles
Oliver, Stephen G.
Pál, Csaba
Papp, Balázs
author_sort Szappanos, Balázs
collection PubMed
description Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments.
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spelling pubmed-31254392012-01-01 An integrated approach to characterize genetic interaction networks in yeast metabolism Szappanos, Balázs Kovács, Károly Szamecz, Béla Honti, Frantisek Costanzo, Michael Baryshnikova, Anastasia Gelius-Dietrich, Gabriel Lercher, Martin J. Jelasity, Márk Myers, Chad L. Andrews, Brenda J. Boone, Charles Oliver, Stephen G. Pál, Csaba Papp, Balázs Nat Genet Article Intense experimental and theoretical efforts have been made to globally map genetic interactions, yet we still do not understand how gene-gene interactions arise from the operation of biomolecular networks. To bridge the gap between empirical and computational studies, we: i) quantitatively measure genetic interactions between ~185,000 metabolic gene pairs in Saccharomyces cerevisiae, ii) superpose the data on a detailed systems biology model of metabolism, and iii) introduce a machine-learning method to reconcile empirical interaction data with model predictions. We systematically investigate the relative impacts of functional modularity and metabolic flux coupling on the distribution of negative and positive genetic interactions. We also provide a mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability. Last, we demonstrate the feasibility of automated metabolic model refinement by correcting misannotations in NAD biosynthesis and confirming them by in vivo experiments. 2011-05-29 /pmc/articles/PMC3125439/ /pubmed/21623372 http://dx.doi.org/10.1038/ng.846 Text en Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Szappanos, Balázs
Kovács, Károly
Szamecz, Béla
Honti, Frantisek
Costanzo, Michael
Baryshnikova, Anastasia
Gelius-Dietrich, Gabriel
Lercher, Martin J.
Jelasity, Márk
Myers, Chad L.
Andrews, Brenda J.
Boone, Charles
Oliver, Stephen G.
Pál, Csaba
Papp, Balázs
An integrated approach to characterize genetic interaction networks in yeast metabolism
title An integrated approach to characterize genetic interaction networks in yeast metabolism
title_full An integrated approach to characterize genetic interaction networks in yeast metabolism
title_fullStr An integrated approach to characterize genetic interaction networks in yeast metabolism
title_full_unstemmed An integrated approach to characterize genetic interaction networks in yeast metabolism
title_short An integrated approach to characterize genetic interaction networks in yeast metabolism
title_sort integrated approach to characterize genetic interaction networks in yeast metabolism
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125439/
https://www.ncbi.nlm.nih.gov/pubmed/21623372
http://dx.doi.org/10.1038/ng.846
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