BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1911140002?filename=Tiwari2019.xmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1911140002?filename=Tiwari2019.sedmlhttps://www.ebi.ac.uk/biomodels/model/download/MODEL1911140002?filename=Tiwari2019.cpsprimaryOK200Krishna Kumar TiwariNon-curatedordinary differential equation modelL2V4https://www.ebi.ac.uk/biomodels/MODEL191114000232573648falseBioModelsSBMLModelsTiwari2019 Mathematical model of NFKB regulatory module with TNF and ROS2020MODEL1911140002Mihai Glont, Chinmay Arankalle, Krishna Kumar Tiwari, Tung Nguyen, Henning Hermjakob, Rahuman S Malik-SheriffMihai Glont32573648, <h4>Motivation</h4>One of the major bottlenecks in building systems biology models is identification and estimation of model parameters for model calibration. Searching for model parameters from published literature and models is an essential, yet laborious task.<h4>Results</h4>We have developed a new service, BioModels Parameters, to facilitate search and retrieval of parameter values from the Systems Biology Markup Language models stored in BioModels. Modellers can now directly search for a model entity (e.g. a protein or drug) to retrieve the rate equations describing it; the associated parameter values (e.g. degradation rate, production rate, Kcat, Michaelis-Menten constant, etc.) and the initial concentrations. Currently, BioModels Parameters contains entries from over 84,000 reactions and 60 different taxa with cross-references. The retrieved rate equations and parameters can be used for scanning parameter ranges, model fitting and model extension. Thus, BioModels Parameters will be a valuable service for systems biology modellers.<h4>Availability and implementation</h4>The data are accessible via web interface and API. BioModels Parameters is free to use and is publicly available at https://www.ebi.ac.uk/biomodels/parameterSearch.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.. 17, 36. European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.ktiwari@ebi.ac.ukEMBL-EBIThe two-feedback-loop regulatory module of nuclear factor kappaB (NF-kappaB) signaling pathway is modeled by means of ordinary differential equations. The constructed model involves two-compartment kinetics of the activators IkappaB (IKK) and NF-kappaB, the inhibitors A20 and IkappaBalpha, and their complexes. In resting cells, the unphosphorylated IkappaBalpha binds to NF-kappaB and sequesters it in an inactive form in the cytoplasm. In response to extracellular signals such as tumor necrosis factor or interleukin-1, IKK is transformed from its neutral form (IKKn) into its active form (IKKa), a form capable of phosphorylating IkappaBalpha, leading to IkappaBalpha degradation. Degradation of IkappaBalpha releases the main activator NF-kappaB, which then enters the nucleus and triggers transcription of the inhibitors and numerous other genes. The newly synthesized IkappaBalpha leads NF-kappaB out of the nucleus and sequesters it in the cytoplasm, while A20 inhibits IKK converting IKKa into the inactive form (IKKi), a form different from IKKn, no longer capable of phosphorylating IkappaBalpha. After parameter fitting, the proposed model is able to properly reproduce time behavior of all variables for which the data are available: NF-kappaB, cytoplasmic IkappaBalpha, A20 and IkappaBalpha mRNA transcripts, IKK and IKK catalytic activity in both wild-type and A20-deficient cells. The model allows detailed analysis of kinetics of the involved proteins and their complexes and gives the predictions of the possible responses of whole kinetics to the change in the level of a given activator or inhibitor.<h4>Motivation</h4>One of the major bottlenecks in building systems biology models is identification and estimation of model parameters for model calibration. Searching for model parameters from published literature and models is an essential, yet laborious task.<h4>Results</h4>We have developed a new service, BioModels Parameters, to facilitate search and retrieval of parameter values from the Systems Biology Markup Language models stored in BioModels. Modellers can now directly search for a model entity (e.g. a protein or drug) to retrieve the rate equations describing it; the associated parameter values (e.g. degradation rate, production rate, Kcat, Michaelis-Menten constant, etc.) and the initial concentrations. Currently, BioModels Parameters contains entries from over 84,000 reactions and 60 different taxa with cross-references. The retrieved rate equations and parameters can be used for scanning parameter ranges, model fitting and model extension. Thus, BioModels Parameters will be a valuable service for systems biology modellers.<h4>Availability and implementation</h4>The data are accessible via web interface and API. BioModels Parameters is free to use and is publicly available at https://www.ebi.ac.uk/biomodels/parameterSearch.<h4>Supplementary information</h4>Supplementary data are available at Bioinformatics online.<h4>Objective</h4>To use a computational approach to investigate the cellular and extracellular matrix changes that occur with age in the knee joints of mice.<h4>Methods</h4>Knee joints from an inbred C57/BL1/6 (ICRFa) mouse colony were harvested at 3-30 months of age. Sections were stained with H&E, Safranin-O, Picro-sirius red and antibodies to matrix metalloproteinase-13 (MMP-13), nitrotyrosine, LC-3B, Bcl-2, and cleaved type II collagen used for immunohistochemistry. Based on this and other data from the literature, a computer simulation model was built using the Systems Biology Markup Language using an iterative approach of data analysis and modelling. Individual parameters were subsequently altered to assess their effect on the model.<h4>Results</h4>A progressive loss of cartilage matrix occurred with age. Nitrotyrosine, MMP-13 and activin receptor-like kinase-1 (ALK1) staining in cartilage increased with age with a concomitant decrease in LC-3B and Bcl-2. Stochastic simulations from the computational model showed a good agreement with these data, once transforming growth factor-β signalling via ALK1/ALK5 receptors was included. Oxidative stress and the interleukin 1 pathway were identified as key factors in driving the cartilage breakdown associated with ageing.<h4>Conclusions</h4>A progressive loss of cartilage matrix and cellularity occurs with age. This is accompanied with increased levels of oxidative stress, apoptosis and MMP-13 and a decrease in chondrocyte autophagy. These changes explain the marked predisposition of joints to develop osteoarthritis with age. Computational modelling provides useful insights into the underlying mechanisms involved in age-related changes in musculoskeletal tissues.Oxidative changes and signalling pathways are pivotal in initiating age-related changes in articular cartilage.Mathematical model of NF-kappaB regulatory module.BioModels Parameters: a treasure trove of parameter values from published systems biology models.Glont Mihai M, Arankalle Chinmay C, Tiwari Krishna K, Nguyen Tung V N TVN, Hermjakob Henning H, Malik-Sheriff Rahuman S RSLipniacki Tomasz T, Paszek Pawel P, Brasier A R Allan R AR, Luxon Bruce B, Kimmel Marek MHui Wang W, Young David A DA, Rowan Andrew D AD, Xu Xin X, Cawston Tim E TE, Proctor Carole J CJfalseTiwari2019 - Mathematical model of NFKB regulatory module with TNF and ROS It is a dynamic model containing elements of two different model as one. It contains the impact of TNF on SOD and impact of ROS on A20 behavior. 2022-03-312022-03-312019-11-14MODEL1911140002SBO:0000393R-HSA-5676594325736481509401525475114Q9Z1E3C132890C39173C64382C18103GO:0005737GO:0005634GO:0034612GO:0030163GO:0006606GO:0005515GO:0006611GO:0006351GO:0043687GO:0006402GO:000641210090Q60680Q9Z1E3P25799Q60769P06804