|   | 
Details
   web
Records
Author Aite, M.; Chevallier, M.; Frioux, C.; Trottier, C.; Got, J.; Cortes, M.P.; Mendoza, S.N.; Carrier, G.; Dameron, O.; Guillaudeux, N.; Latorre, M.; Loira, N.; Markov, G.V.; Maass, A.; Siegel, A.
Title Traceability, reproducibility and wiki-exploration for “a-la-carte” reconstructions of genome-scale metabolic models Type
Year 2018 Publication Plos Computational Biology Abbreviated Journal PLoS Comput. Biol.
Volume 14 Issue 5 Pages (down) 25 pp
Keywords
Abstract Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent model s result from “a la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.
Address [Aite, Meaziane; Chevallier, Marie; Frioux, Cleamence; Trottier, Camille; Got, Jeanne; Dameron, Olivier; Guillaudeux, Nicolas; Siegel, Anne] Univ Rennes, INRIA, CNRS, IRISA, Rennes, France, Email: anne.siegel@irisa.fr
Corporate Author Thesis
Publisher Public Library Science Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1553-734X ISBN Medium
Area Expedition Conference
Notes WOS:000434012100025 Approved
Call Number UAI @ eduardo.moreno @ Serial 879
Permanent link to this record
 

 
Author Loira, N.; Mendoza, S.; Cortes, M.P.; Rojas, N.; Travisany, D.; Di Genova, A.; Gajardo, N.; Ehrenfeld, N.; Maass, A.
Title Reconstruction of the microalga Nannochloropsis salina genome-scale metabolic model with applications to lipid production Type
Year 2017 Publication Bmc Systems Biology Abbreviated Journal BMC Syst. Biol.
Volume 11 Issue Pages (down) 17 pp
Keywords Genome-scale Metabolic model; Nannochloropsis salina; TAG; Microalg ae
Abstract Background: Nannochloropsis salina (= Eustigmatophyceae) is a marine microalga which has become a biotechnological target because of its high capacity to produce polyunsaturated fatty acids and triacylglycerols. It has been used as a source of biofuel, pigments and food supplements, like Omega 3. Only some Nannochloropsis species have been sequenced, but none of them benefit from a genome-scale metabolic model (GSMM), able to predict its metabolic capabilities. Results: We present iNS934, the first GSMM for N. salina, including 2345 reactions, 934 genes and an exhaustive description of lipid and nitrogen metabolism. iNS934 has a 90% of accuracy when making simple growth/no-growth predictions and has a 15% error rate in predicting growth rates in different experimental conditions. Moreover, iNS934 allowed us to propose 82 different knockout strategies for strain optimization of triacylglycerols. Conclusions: iNS934 provides a powerful tool for metabolic improvement, allowing predictions and simulations of N. salina metabolism under different media and genetic conditions. It also provides a systemic view of N. salina metabolism, potentially guiding research and providing context to -omics data.
Address [Loira, Nicolas; Mendoza, Sebastian; Paz Cortes, Maria; Travisany, Dante; Di Genova, Alex; Maass, Alejandro] Univ Chile, Ctr Math Modeling, Math, Beauchef 851,7th Floor, Santiago, Chile, Email: nloira@dim.uchile.cl
Corporate Author Thesis
Publisher Biomed Central Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1752-0509 ISBN Medium
Area Expedition Conference
Notes WOS:000404918700001 Approved
Call Number UAI @ eduardo.moreno @ Serial 744
Permanent link to this record
 

 
Author Cortes, M.P.; Mendoza, S.N.; Travisany, D.; Gaete, A.; Siegel, A.; Cambiazo, V.; Maass, A.
Title Analysis of Piscirickettsia salmonis Metabolism Using Genome-Scale Reconstruction, Modeling, and Testing Type
Year 2017 Publication Frontiers In Microbiology Abbreviated Journal Front. Microbiol.
Volume 8 Issue Pages (down) 15 pp
Keywords pathogen; genome-scale; metabolism; constraint-based; Piscirickettsia; salmonis
Abstract Piscirickettsia salmonis is an intracellular bacterial fish pathogen that causes piscirickettsiosis, a disease with highly adverse impact in the Chilean salmon farming industry. The development of effective treatment and control methods for piscireckttsiosis is still a challenge. To meet it the number of studies on P. salmonis has grown in the last couple of years but many aspects of the pathogen's biology are still poorly understood. Studies on its metabolism are scarce and only recently a metabolic model for reference strain LF-89 was developed. We present a new genomescale model for P. salmonis LF-89 with more than twice as many genes as in the previous model and incorporating specific elements of the fish pathogen metabolism. Comparative analysis with models of different bacterial pathogens revealed a lower flexibility in P. salmonis metabolic network. Through constraint-based analysis, we determined essential metabolites required for its growth and showed that it can benefit from different carbon sources tested experimentally in new defined media. We also built an additional model for strain A1-15972, and together with an analysis of P. salmonis pangenome, we identified metabolic features that differentiate two main species clades. Both models constitute a knowledge-base for P. salmonis metabolism and can be used to guide the efficient culture of the pathogen and the identification of specific drug targets.
Address [Cortes, Maria P.; Mendoza, Sebastian N.; Travisany, Dante; Maass, Alejandro] Univ Chile, Ctr Math Modeling, Math, Santiago, Chile, Email: mpcortes@dim.uchile.cl
Corporate Author Thesis
Publisher Frontiers Media Sa Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1664-302x ISBN Medium
Area Expedition Conference
Notes WOS:000417578100001 Approved
Call Number UAI @ eduardo.moreno @ Serial 787
Permanent link to this record