{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Svg":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007.svg"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007-biopax3.owl"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007_urn.xml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007.vcml","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007.sci","https://www.ebi.ac.uk/biomodels/model/download/MODEL1604280007?filename=MODEL1604280007.xpp"]},"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/MODEL1604280007"],"publication_pubmed":["27895706"],"isPrivate":["false"],"repository":["BioModels"],"non_derived_xrefs":["MODEL1302010004 biomodels.db"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Castillo2016   Whole genome metabolic model of P.guilliermondii using CoReCo"],"publication_year":["2016"],"submissionId":["MODEL1604280007"],"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                            <h4>Background</h4><i>Trichoderma reesei</i> 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.<h4>Results</h4>A whole-genome metabolic model of <i>T. reesei</i> 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 <i>T. reesei</i> has been measured experimentally to build and include a specific biomass equation in the model.<h4>Conclusions</h4>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 <i>T. reesei</i> 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 <i>T. reesei</i> 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":["<h4>Background</h4><i>Trichoderma reesei</i> 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.<h4>Results</h4>A whole-genome metabolic model of <i>T. reesei</i> 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 <i>T. reesei</i> has been measured experimentally to build and include a specific biomass equation in the model.<h4>Conclusions</h4>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 <i>T. reesei</i> 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 <i>T. reesei</i> model is ready to be used for simulations of protein production processes."],"pubmed_title":["Whole-genome metabolic model of <i>Trichoderma reesei</i> 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"],"description_synonyms":["scale tissue, Phylogenetic Reconstruction, Procedures, Phylogenetic Structure, peltate hair, Gene, Phylogenetic Analysis, Cost Comparisons, protein, Network, Measure, protein-containing complex, Cost-Minimization, BLAST, Distance, Molecular Biologies, Phylogenomics, DOI, Phylogenetic Signals, method, Techniques, Blasts, Phylogenetic Comparative Methods, Phylogenetic Inference, Bio Informatics, Inference, posterior, Cost Minimization Analysis, Method, method used in an experiment, Relatedness, Studies, Gene Products, Molecular Phylogenetics, Comparative Analysis, Phylogenetic Relatednesses, Low, Phylogenetic Relatedness, Analysis, Phylogenetic Comparative Analysis, protein aggregate, Cost Comparison, Technique, CD224, Phylogenetic Relationship, doi, Community Phylogenetics, Group, Hypocrea jecorina, Phylogenomic, me75, D22S732, hematoblast, Genomes, Biology, Analyses, Molecular, Cost Measures, plant peltate hair, Cost-Minimization Analyses, Affordability, Signal, Measures, Comparison, financing, Phylogenetic Networks, Phylogenetic Distances, Bio-Informatic, Structure, D17Mit170, T1, Computational, Probabilities, SLAMF2, Reconstruction, Study, Cost Analysis, funding, reaction, Methodological Studies, Phylogenetic Network, hemocytoblast, Bio-Informatics, Phylogenetic Reconstructions, blast, Phylogenetic Distance, Phylogenetic Incongruences, Incongruence, Hypocea jecorina, hematocytoblast, Phylogenetic Clustering, species, GGT, Comparisons, Relationship, Affordabilities, Computational Molecular Biologies, posterior end of organism, GGT 1., Phylogenetic Comparative Method, fees, Molecular Phylogenetic, D22S672, Molecular Biology, cou, Phylogenetic Analyse, MEM-102, protein complex, mCD48, GGT 1, Proteins, Biologies, Phylogenetic Inferences, Procedure, Tree, Phylogenetic Analyses, Tl3, BCM1, Phylogenetic Comparative, Tl2, Phylogenetic Clusterings, Computational Molecular, Bioinformatics, Phylogenetic Generalized Least Squares, Lr, native protein, Trichoderma reesei, Protein, Computational Molecular Biology, sequence, Phylogenetic Comparative Analyses, BLAST1, scales, Phylogenetic Incongruence, finances, Data Base, Costs and Cost Analyses, Costs, Phylogenetic Group, Phylogenetic Tree, Bioinformatic, scale, Cost, Hypocrea pecorina, Cost-Minimization Analysis, salaries, hCD48, Phylogenies, Phylogenetic Signal, Methodological, Phylogenetic Structures, whole genome, Pricing, Methodological Study, primary structure of sequence macromolecule, plan specification, Protein Gene Products, Analyse, Gene Proteins, Phylogenetic Trees, cost, Phylogenetic, Comparative Method, Clustering, Community Phylogenetic, Phylogenetic Relationships, Bra, Phylogenetic Groups, financial management, Cost Measure, GTG, Community, Cost Analyses"],"name_synonyms":["whole genome, Genomes."],"pubmed_abstract_synonyms":["biochemical pathways, Sectors, Metabolic Process, rasGAP, multi-cellular organism, GRP1/cytohesin 1, Procedures, determination, Biocatalysts, Processes, GAPDH II, Tertiary Sectors, Metabolic Concepts, 10538, GTPase-activating protein, Gene, Gapdh13F, protein, Metabolic Processes, GAP1, protein-containing complex, Productivity, protrusion, CG11633, cytohesin/GRP1, method, Techniques, GADPH, CG8893, Method, gap1, Metabolism, method used in an experiment, GRP1, Grp1, Studies, Gene Products, Concepts, PKWS, Metabolism Concept, protein aggregate, animal, Phenomenon, Metabolism Phenomena, FBgn 32821, Technique, Industries, Hypocrea jecorina, enzymes, Genomes, RasGAP, catabolism, Gapd, GA3PDH, PTPSTEP, Gap 1, l(2)SH2 0323, Metabolic Concept, mip, metabolic process resulting in cell growth, GAPDH2, Study, reaction, d-CdGAPr, Enzyme, Gapdh-2, Neural-specific protein-tyrosine phosphatase, Methodological Studies, l(2)k08110, biotransformation, CM-AVM, CG6721, Hypocea jecorina, species, Catabolism, DmelCG8893, GPH, anatomical protrusion, degradation, Process, body, protein complex, metabolism resulting in cell growth, Proteins, whole body, stepk, enzyme activity, Procedure, RASGAP, RASA, results, Concept, Metabolic Phenomena, Metabolism Concepts, organism, uptake, Tertiary, native protein, Gapdh, Trichoderma reesei, Algorithm, chemical analysis, sxt, Protein, Phenomena, 3.1.3.48, secretion, spine., background, rI533, GAP, Gap, p120GAP, p120RASGAP, metabolism, l(2)SH0323, Data Base, GAPDH, Metabolic Phenomenon, CYH1, Ras-GAP, multicellular organism metabolic process, CG10538, DmelCG6721, whole organism, biodegradation, Metabolic, Step, CG11628, Hypocrea pecorina, DmelCG10538, Striatum-enriched protein-tyrosine phosphatase, whole genome, Methodological, gap, CMAVM, Methodological Study, introduction, DmelCG11628, plan specification, Protein Gene Products, Biotechnologies, Gene Proteins, Sector, Biomasses, STEP, Ras p21 protein activator, Koerper, Biocatalyst, Tertiary Sector, assay, Anabolism"],"pubmed_title_synonyms":["Hypocrea jecorina, Hypocea jecorina, whole genome, Genomes, Trichoderma reesei, Hypocrea pecorina."],"additional_accession":[]},"is_claimable":false,"name":"Castillo2016 - Whole-genome metabolic model of P.guilliermondii 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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