{"database":"BioModels","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Txt":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=curation_notes.txt"],"Pdf":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588.pdf"],"Owl":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588-biopax2.owl","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588-biopax3.owl"],"Svg":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588.svg"],"Xml":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=manifest.xml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588_url.xml"],"Other":["https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588.ode","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588-octave.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=curation_image.png","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588-matlab.m","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=BIOMD0000000588_url.sedml","https://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000588?filename=metadata.rdf"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"submitter":["Vijayalakshmi Chelliah"],"curationStatus":["Manually curated"],"modellingApproach":["ordinary differential equation model"],"levelVersion":["L2V4"],"full_dataset_link":["https://www.ebi.ac.uk/biomodels/BIOMD0000000588"],"publication_pubmed":["24427523"],"isPrivate":["false"],"repository":["BioModels"],"non_derived_xrefs":["BIOMD0000000049 biomodels.db"],"omics_type":["Models"],"modelFormat":["SBML"],"tokenised_name":["Benson2013   Identification of key drug targets in nerve growth factor pathway"],"publication_year":["2013"],"submissionId":["MODEL1601290000"],"first_author":["Neil Benson"],"publication_authors":["Neil Benson, Tomomi Matsuura, Sergey Smirnov, Oleg Demin, Hannah M Jones, Pinky Dua, Piet H van der Graaf"],"publication":["24427523,\n                            The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.. 2, 3.\n                            Xenologiq Ltd, Unit 7 , Denne Hill Business Park, Canterbury CT4 6HD , UK ; Department of Pharmacokinetics, Dynamics and Metabolism , Pfizer Worldwide R&D , Boston, MA , USA."],"submitter_mail":["viji@ebi.ac.uk"],"submitter_affiliation":["EMBL-EBI"],"publicationId":["BIOMD0000000588"],"pubmed_abstract":["The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs.","To comprehend the Ras/ERK MAPK cascade, which comprises Ras, Raf, MEK, and ERK, several kinetic simulation models have been developed. However, a large number of parameters that are essential for the development of these models are still missing and need to be set arbitrarily. Here, we aimed at collecting these missing parameters using fluorescent probes. First, the levels of the signaling molecules were quantitated. Second, to monitor both the activation and nuclear translocation of ERK, we developed probes based on the principle of fluorescence resonance energy transfer. Third, the dissociation constants of Ras.Raf, Raf.MEK, and MEK.ERK complexes were estimated using a fluorescent tag that can be highlighted very rapidly. Finally, the same fluorescent tag was used to measure the nucleocytoplasmic shuttling rates of ERK and MEK. Using these parameters, we developed a kinetic simulation model consisting of the minimum essential members of the Ras/ERK MAPK cascade. This simple model reproduced essential features of the observed activation and nuclear translocation of ERK. In this model, the concentration of Raf significantly affected the levels of phospho-MEK and phospho-ERK upon stimulation. This prediction was confirmed experimentally by decreasing the level of Raf using the small interfering RNA technique. This observation verified the usefulness of the parameters collected in this study."],"pubmed_title":["Dynamics of the Ras/ERK MAPK cascade as monitored by fluorescent probes.","Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology."],"pubmed_authors":["Benson Neil N, Matsuura Tomomi T, Smirnov Sergey S, Demin Oleg O, Jones Hannah M HM, Dua Pinky P, van der Graaf Piet H PH","Fujioka Aki A, Terai Kenta K, Itoh Reina E RE, Aoki Kazuhiro K, Nakamura Takeshi T, Kuroda Shinya S, Nishida Eisuke E, Matsuda Michiyuki M"],"additional_accession":[]},"is_claimable":false,"name":"Benson2013 - Identification of key drug targets in nerve growth factor pathway","description":"\n      \n    Benson2013 - Identification of key drug targets in nerve growth factor pathway\n\n  This model is described in the article:\n  \n    \n    Systems pharmacology of the nerve growth factor pathway: use of a systems biology model for the identification of key drug targets using sensitivity analysis and the integration of physiology and pharmacology. \n  \n  Benson N, Matsuura T, Smirnov S, Demin O, Jones HM, Dua P, van der Graaf PH. \n  \n  Interface Focus 2013 Apr; 3(2): 20120071 \n  \n  Abstract:\n  \n    \n    The nerve growth factor (NGF) pathway is of great interest as a potential source of drug targets, for example in the management of certain types of pain. However, selecting targets from this pathway either by intuition or by non-contextual measures is likely to be challenging. An alternative approach is to construct a mathematical model of the system and via sensitivity analysis rank order the targets in the known pathway, with respect to an endpoint such as the diphosphorylated extracellular signal-regulated kinase concentration in the nucleus. Using the published literature, a model was created and, via sensitivity analysis, it was concluded that, after NGF itself, tropomyosin receptor kinase A (TrkA) was one of the most sensitive druggable targets. This initial model was subsequently used to develop a further model incorporating physiological and pharmacological parameters. This allowed the exploration of the characteristics required for a successful hypothetical TrkA inhibitor. Using these systems models, we were able to identify candidates for the optimal drug targets in the known pathway. These conclusions were consistent with clinical and human genetic data. We also found that incorporating appropriate physiological context was essential to drawing accurate conclusions about important parameters such as the drug dose required to give pathway inhibition. Furthermore, the importance of the concentration of key reactants such as TrkA kinase means that appropriate contextual data are required before clear conclusions can be drawn. Such models could be of great utility in selecting optimal targets and in the clinical evaluation of novel drugs. \n  \n\n\n  This model is hosted on \n  BioModels Database\n  and identified by: \n  BIOMD0000000588.\n  To cite BioModels Database, please use: \n  BioModels Database:\n  An enhanced, curated and annotated resource for published\n  quantitative kinetic models.\n\n\n  To the extent possible under law, all copyright and related or\n  neighbouring rights to this encoded model have been dedicated to\n  the public domain worldwide. Please refer to \n  CC0\n  Public Domain Dedication for more information.\n\n\n    ","dates":{"last_modification":"2024-08-21","publication":"2024-09-02","submission":"2016-01-29"},"accession":"BIOMD0000000588","cross_references":{"pubmed":["24427523","16418172"],"chebi":["CHEBI:52217"],"biomodels__db":["MODEL1601290000","BIOMD0000000588"],"go":["GO:0051387","GO:0038180"],"taxonomy":["10114"],"bto":["BTO:0001009"],"efo":["EFO:EFO:0003843"]}}