{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035.pdf"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035.svg"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035-biopax3.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035-biopax2.owl"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035_urn.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035.m","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035.xpp","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035.png","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280035?filename=MODEL1604280035.vcml"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Vijayalakshmi Chelliah"],"curationStatus":["Non-curated"],"modellingApproach":["constraint-based model"],"levelVersion":["L3V1"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/MODEL1604280035"],"publication_pubmed":["27895706"],"isPrivate":["false"],"repository":["BioModels"],"non_derived_xrefs":["MODEL1302010030 biomodels.db"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Castillo2016 Whole genome metabolic model of E.cuniculi using CoReCo"],"publication_year":["2016"],"submissionId":["MODEL1604280035"],"modelFlag":["Non Kinetic"],"first_author":["Sandra Castillo"],"publication_authors":["Sandra Castillo, sandra.castillo@vtt.fi, Dorothee Barth, Mikko Arvas, Tiina M Pakula, Esa Pitkänen, Peter Blomberg, Tuulikki Seppanen-Laakso, Heli Nygren, Dhinakaran Sivasiddarthan, Merja Penttilä, Merja Oja"],"publication":["27895706,\n Background
Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.Results
A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.Conclusions
The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes.. null, 9.\n VTT Technical Research Centre of Finland, Tietotie 2, P.O. Box FI-1000, 02044 Espoo, Finland."],"submitter_mail":["viji@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"pubmed_abstract":["Background
Trichoderma reesei is one of the main sources of biomass-hydrolyzing enzymes for the biotechnology industry. There is a need for improving its enzyme production efficiency. The use of metabolic modeling for the simulation and prediction of this organism's metabolism is potentially a valuable tool for improving its capabilities. An accurate metabolic model is needed to perform metabolic modeling analysis.Results
A whole-genome metabolic model of T. reesei has been reconstructed together with metabolic models of 55 related species using the metabolic model reconstruction algorithm CoReCo. The previously published CoReCo method has been improved to obtain better quality models. The main improvements are the creation of a unified database of reactions and compounds and the use of reaction directions as constraints in the gap-filling step of the algorithm. In addition, the biomass composition of T. reesei has been measured experimentally to build and include a specific biomass equation in the model.Conclusions
The improvements presented in this work on the CoReCo pipeline for metabolic model reconstruction resulted in higher-quality metabolic models compared with previous versions. A metabolic model of T. reesei has been created and is publicly available in the BIOMODELS database. The model contains a biomass equation, reaction boundaries and uptake/export reactions which make it ready for simulation. To validate the model, we dem1onstrate that the model is able to predict biomass production accurately and no stoichiometrically infeasible yields are detected. The new T. reesei model is ready to be used for simulations of protein production processes."],"pubmed_title":["Whole-genome metabolic model of Trichoderma reesei built by comparative reconstruction."],"pubmed_authors":["Castillo Sandra S, Castillo Sandra S, Barth Dorothee D, Arvas Mikko M, Pakula Tiina M TM, Pitkänen Esa E, Blomberg Peter P, Seppanen-Laakso Tuulikki T, Nygren Heli H, Sivasiddarthan Dhinakaran D, Penttilä Merja M, Oja Merja M"],"additional_accession":[]},"is_claimable":false,"name":"Castillo2016 - Whole-genome metabolic model of E.cuniculi using CoReCo","description":"\n \n \n This model was reconstructed with CoReCo method from protein sequence and phylogeny data. CoReCo is described in Pitkanen, E., Jouhten, P., Hou, J., Syed, M. F., Blomberg, P., Kludas, J., Oja, M., Holm, L., Penttila, M., Rousu, J. and Arvas, M. (2014). Comparative Genome-Scale Reconstruction of Gapless Metabolic Networks for Present and Ancestral Species. PLoS Computational Biology, 10(2), e1003465. doi:10.1371/journal.pcbi.1003465 and Castillo, S., Barth, D., Arvas, M., Pakula, TM., Pitkanen, E., Blomberg, P., Penttila M., Oja M. Whole genome metabolic model of Trichoderma reesei built by comparative reconstruction.\n CoReCo annotates each reaction with the following attributes: \n \n reco:balanced: True, if reaction is in stoichiometric balance.\n reco:posterior: CoReCo phase II probability\n reco:cost: CoReCo phase II log-score\n reco:level: High, if cost below reconstruction threshold, and Low, if cost above reconstruction threshold. A reaction with level=Low only appears in reconstruction to gapfill a reaction with level=High.\n reco:naivep: CoReCo phase I probability.\n reco:btscore: CoReCo phase II probability considering only BLAST as input.\n reco:gtscore: CoReCo phase II probability considering only GTG as input.\n reco:bscore: CoReCo BLAST score\n reco:bseq1: Protein sequence in target species with highest BLAST score\n reco:bseq2: Protein sequence in UniProt with highest BLAST score\n reco:gscore: CoReCo GTG score\n reco:gseq1: Protein sequence in target species\n reco:gseq2: Protein sequence in GTG database with highest GTG score\n \n \n 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