BioModelsapplication/xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=curation_notes.txthttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229.pdfhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229-biopax3.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229-biopax2.owlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229_url.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=manifest.xmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229.pnghttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=metadata.rdfhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229.vcmlhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229.odehttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229-octave.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229-matlab.mhttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229.scihttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=curation_image.pnghttps://www.ebi.ac.uk/biomodels/model/download/BIOMD0000000229?filename=BIOMD0000000229_url.sedmlprimaryOK200Vijayalakshmi ChelliahManually curatedordinary differential equation modelL2V4https://www.ebi.ac.uk/biomodels/BIOMD000000022912482327falseBioModelsBIOMD0000000099 biomodels.dbModelsSBMLMa2002 cAMP oscillations2002MODEL0606755064Lan MaLan Ma, Pablo A Iglesias12482327, <h4>Background</h4>Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed.<h4>Results</h4>Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary.<h4>Conclusion</h4>The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.. null, 3. Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD USA. lma@jhu.eduviji@ebi.ac.ukEMBL-EBIBIOMD0000000229<h4>Background</h4>Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed.<h4>Results</h4>Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary.<h4>Conclusion</h4>The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness.Quantifying robustness of biochemical network models.Ma Lan L, Iglesias Pablo A PAVAL, IBGC5, Presented, Quantification, Important, Clo, cycline, Procedures, determination, Math, SLF, Different, DmcyclinE, fond, Importance, Biochemical, cycE, CycEI, model, contrasted, l(2)br37, prevention, CYCLE, cdi7, Techniques, Structural, Evaluation Procedure, Ccne, cyclinE, Presenting, Method, sensitive, Studies, Cdi7, CDI7, Importance Rating Score 0, PDGF2, Useful, Models, prevention and control, Technique, sensitivity, Means, Modeling System, CYCE, reference sample, occurrence, DmelCG3938, CyclE, prevalence, SF, 3938, Biochemical Diagnosis, procedures, Robustness., DmcycE, Structure, Very poor, Study, l(2)k05007, preventive measures, allergic reaction, Con, dm-cycE, Methodological Studies, Bifurcation, Mathematical, Presentation, blz, Very Poor, Usefulness, Sensitivity, Analytical Procedure Robustness, Model, Sl, Parameter, Parameter Value, Importance Score 0, incidence, Controlled, SINGLE, Controlling, preventive therapy, Bound, Programming Parameter, Difference, frequency, Importance 0, Procedure, Cyc E, br37, results, Tool, Conclusion, Evaluation, l(2)k02514, Evaluated, DmCycE, BG:DS07108.3, Population Parameter, chemical analysis, Input Parameter, Conflict, background, techniques, CyeE, Value, l(2)05206, outbreaks, Quantify, SIS, Biochemical Markers Diagnosis, Single Person, Argument, Biochemical Response, Scientific Equipment, Evaluate, l(2)k02602, Model System, PARM, Mathematics, Quantitation, Engineerings, Limited, prophylaxis, conclusion, l(2)35Dd, Specificity, Methodological, parameter, Biochemical Evidence of Disease, Robustness, Methodological Study, Present, surveillance, PDGF-2, morbidity, endemics, value, introduction, D-CycE, BED-Biochemical Evidence of Disease, Kitlg, Differential, l35Dd, Only, Specificity and Sensitivity, control, Mgf, Population Measure, Solitary, epidemics, Equipment, Single, assay, SCF, SSV, CG3938, c-sis, Gb, Limit, Alone, methodologyfs(1)A384, dec, determination, fond, AUTSX5, nip, E-Cadherin, NOVH, CycEI, CCN3, QM, prevention, dmTAF[[II]]230, fs(1)A257, Ccne, B1, l(2)SH1330, TEP1, fs(1)5, PDGF2, prevention and control, fs(1)M104, fs(1)M102, Hyalostilbum, dec1, fs(1)14-963, reference sample, TFIID TAF250, cel, fs(1)11-549, DCAD2, DCad2, 35Bb, DECad, free, CG3722, IBP-9, preventive measures, allergic reaction, DE Cad, DE-CAD2, dEcad, acidos nucleicos, Dictyostelium, fs(1)M1021, DECadh, NOVh, DEcad, fs(1)12-3907, DE, dTAF[[II]]230, preventive therapy, Papers, PubMed, frequency, TAF200, Shg, DE-Cad2, TAFII-250, Cyc E, br37, TAF250/230, results, fs(1)12-1873, DE-cadh, fs(1)1501, fs(1)12-365, TAFII250, l(2)br3, l35Bb, BG:DS07108.3, PTEN1, ECadh, DE-Cadherin, nucleic acids, DE-cadherin, NOV, l(2)05206, PlexA1, Nucleic., SIS, Acid, shg/DE-Cad, Plxn1, D E-cad, Engineerings, E-cadherin, MMAC1, nov, discoideum, CG17603, surveillance, PDGF-2, TAF[[II]], gp150, morbidity, mKIAA4053, Fc, fs(1)Y[b], Nucleic, l(2)SH2 1330, l35Dd, Specificity and Sensitivity, Taf250, DmelCG3722, SR3-5, C130088N23Rik, PLXN1, Acids, Fc1, BG:DS01219.1, DXS648E, CT12481, BZS, com5, DCad, ECad2, TAF230, fs(1)12-2514, IBGC5, d230, cycline, DE Cadh, YB, GLM2, mol, DmcyclinE, fs(1)14A-114, fs(1)384, number, dTAFII250, cycE, Copyrights, EfW1, DmelCG4482, presence, l(2)br37, CYCLE, DmelCG2175, l(2)10469, l(2)k03401, cdi7, MHAM, acides nucleiques, cyclinE, Publication, Yb, dmTAF1, sensitive, Taf230, Cdi7, CDI7, sensitivity, CG2706, l(2)br23, TAF250, CadE, Ecad, Taf200, CYCE, dTAF[[II]]250, occurrence, cadh, cell, DmelCG3938, CyclE, Dictyostelium discoideum, prevalence, Dictyosteliums, 3938, Taf1p, Fcp7C, IGFBP9, DmcycE, fs(1)12-3512, ECAD, ECad, l(2)k05007, dTAF250, Kiaa4053, dm-cycE, Dictyostelium discoideums, Abstract, D-cad, L10, Sensitivity, TAF, CADH, incidence, Controlled, DE[cyto], Controlling, TAF[[II]]250, dec[[1]], fs(1)13C-73, CG4482, acide nucleique, DmelCG2706, DE-cad, l(2)35Bb, DXS648, l(3)84Ab, CG2175, BG:DS00004.13, Cadh, Cell, dTAF230, fs(1)C1, fs(1)12-403, count in organism, N-cad, Cad, l(2)k02514, DmCycE, fs(1)12-4860, IGFBP-9, p230, Nucleic Acid, CWS1, chemical analysis, DE-CAD, DE-Cad, TAF[[II]]250/230, TFIID, NA, fs(1)14E19, background, CyeE, outbreaks, Data Base, Taf[[II]]250, cad, l(2)k02602, TAF[[II]]230, Nukleinsaeure, 10q23del, prophylaxis, l(2)35Dd, Specificity, TAF[II]250, acido nucleico, CG15268, E-cad, endemics, introduction, D-CycE, br3, DEC, DmelCG17603, control, Nukleinsaeuren, EG:95B7.8, fs(1)13-453, fs(1)13C-57, epidemics, 2600013D04Rik, NIP, assay, SSV, CG3938, c-sis, e-cad, E-CAD, E-Cad, l(2)k10220, TAF1Centers, Networks, BED-Biochemical Evidence of Disease, Modeling System, Biochemical Response, and Consortia, Model System, Network Interface, Consortium or Network, Network Device, Biochemical, Biochemical Diagnosis, Network, Analytical Procedure Robustness, Biochemical Evidence of Disease, Consortium, Model, Robustness, model, Biochemical Markers Diagnosis, Models., NCI Consortium or NetworkfalseMa2002_cAMP_oscillations This a model from the article: Quantifying robustness of biochemical network models. Ma L, Iglesias PA. BMC Bioinformatics.2002 Dec 13;3:38. 12482327, Abstract: BACKGROUND: Robustness of mathematical models of biochemical networks is important for validation purposes and can be used as a means of selecting between different competing models. Tools for quantifying parametric robustness are needed. RESULTS: Two techniques for describing quantitatively the robustness of an oscillatory model were presented and contrasted. Single-parameter bifurcation analysis was used to evaluate the stability robustness of the limit cycle oscillation as well as the frequency and amplitude of oscillations. A tool from control engineering--the structural singular value (SSV)--was used to quantify robust stability of the limit cycle. Using SSV analysis, we find very poor robustness when the model's parameters are allowed to vary. CONCLUSION: The results show the usefulness of incorporating SSV analysis to single parameter sensitivity analysis to quantify robustness. This model is originally proposed by Laub and Loomis (1998).[Laub MT, Loomis WF (1998). A molecular network that produces spontaneous oscillations in excitable cells of Dictyostelium. Mol Biol Cell. 9(12):3521-32. PubMED: 12482327. The parameters used in this model (Ma and Iglesias, 2002), are different from that used in the original model (Laub and Loomis, 1998), because of the typographical errors in the original paper. The parameters used in the model presented by Ma and Iglesias, are obtained directly from the authors of original publication (Laub and Loomis, 1998). These parameters are also used in the website for the Laub-Loomis model, http://www-biology.ucsd.edu/labs/loomis/network/laubloomis.html. By using this model, Kim et al., 2006 [Kim J, Bates DG, Postlethwaite I, Ma L, Iglesias PA. (2006) Robustness analysis of biochemical network models. Syst Biol (Stevenage). 153(3):96-104. PubMED: 16984084], validate and extend the analysis approach proposed by Ma and Iglesias (2002), by showing how hybrid optimisation can be used to compute worst-case parameter combinations in the model. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691. 2024-08-212024-09-022009-08-18BIOMD0000000229xcc0203012482327CHEBI:17489MODEL0606755064BIOMD0000000229GO:0019933GO:0006935GO:0045858GO:0045857GO:0006171GO:0006198C00057544689P13773Q01386Q54QB1Q23917IPR008172IPR002373IPR008349