2021
Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI
Degasperi A, Nguyen L, Fey D, Kholodenko B. Relationship Between Dimensionality and Convergence of Optimization Algorithms: A Comparison Between Data-Driven Normalization and Scaling Factor-Based Methods Using PEPSSBI. Methods In Molecular Biology 2021, 2385: 91-115. PMID: 34888717, PMCID: PMC9446379, DOI: 10.1007/978-1-0716-1767-0_5.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputer SimulationModels, BiologicalSignal TransductionSoftwareSystems BiologyConceptsUnknown parametersOrdinary differential equation modelDifferential equation modelMulti-CPU clusterObjective function constructionModel importParameter estimation softwareParameter identifiabilityOptimization algorithmDNS approachPersonal computerFirst softwareConvergence timeEstimation softwareFunction constructionFactor-based methodsDynamic modelAdditional parametersBiological dataData normalizationAlgorithmSoftwareScaling factorsDNSSimulations
2019
Mapping connections in signaling networks with ambiguous modularity
Lill D, Rukhlenko O, Mc Elwee A, Kashdan E, Timmer J, Kholodenko B. Mapping connections in signaling networks with ambiguous modularity. Npj Systems Biology And Applications 2019, 5: 19. PMID: 31149348, PMCID: PMC6533310, DOI: 10.1038/s41540-019-0096-1.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyComputer SimulationGene Regulatory NetworksModels, BiologicalProtein Interaction MapsProteinsSignal TransductionConceptsModular Response AnalysisProtein abundanceProtein complexesNetwork reconstructionDownstream modulesRetroactive interactionsUpstream moduleComputational restorationNetwork modulesSuite of methodsAbundanceSuch complexesExperimental approachComplexesProteinEnzymePathwaySequestration effectNetwork responseDifferent modules
2018
Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction
Thomaseth C, Fey D, Santra T, Rukhlenko OS, Radde NE, Kholodenko BN. Impact of measurement noise, experimental design, and estimation methods on Modular Response Analysis based network reconstruction. Scientific Reports 2018, 8: 16217. PMID: 30385767, PMCID: PMC6212399, DOI: 10.1038/s41598-018-34353-3.Peer-Reviewed Original ResearchConceptsModular Response AnalysisNetwork reconstructionSteady-state response curvesStatistical conceptsMeasurement noisePropagation of noiseNoise settingsEstimation methodNetwork structureTerms of accuracyLarge perturbationsResponse analysisDifferent replicatesPerturbation dataRegression strategyNoise
2015
Network-based identification of feedback modules that control RhoA activity and cell migration
Kim TH, Monsefi N, Song JH, von Kriegsheim A, Vandamme D, Pertz O, Kholodenko BN, Kolch W, Cho KH. Network-based identification of feedback modules that control RhoA activity and cell migration. Journal Of Molecular Cell Biology 2015, 7: 242-252. PMID: 25780058, DOI: 10.1093/jmcb/mjv017.Peer-Reviewed Original ResearchConceptsRho family GTPasesCancer cell migrationCell migrationRhoA activityControl cell migrationBoolean network modelNetwork-based identificationPotential new targetsRho activationGTPasesSrc inhibitionGenetic backgroundCsk inhibitionMost cancer deathsActivation stateNew targetsNew insightsMigrationCskSrcFAKRewiringInhibitionEGFProtrusion
2009
Positional Information Generated by Spatially Distributed Signaling Cascades
Muñoz-García J, Neufeld Z, Kholodenko BN. Positional Information Generated by Spatially Distributed Signaling Cascades. PLOS Computational Biology 2009, 5: e1000330. PMID: 19300504, PMCID: PMC2654021, DOI: 10.1371/journal.pcbi.1000330.Peer-Reviewed Original ResearchMolecular Dynamics Simulations Reveal that Tyr-317 Phosphorylation Reduces Shc Binding Affinity for Phosphotyrosyl Residues of Epidermal Growth Factor Receptor
Suenaga A, Hatakeyama M, Kiyatkin AB, Radhakrishnan R, Taiji M, Kholodenko BN. Molecular Dynamics Simulations Reveal that Tyr-317 Phosphorylation Reduces Shc Binding Affinity for Phosphotyrosyl Residues of Epidermal Growth Factor Receptor. Biophysical Journal 2009, 96: 2278-2288. PMID: 19289054, PMCID: PMC2717265, DOI: 10.1016/j.bpj.2008.11.018.Peer-Reviewed Original ResearchConceptsSrc homology 2Epidermal growth factor receptorGrowth factor receptorPhospho-tyrosine binding (PTB) domainsLinker regionFull-length ShcPhospho-tyrosine residuesKey conformational changesFactor receptorShc interactionTyr-317Protein ShcTyrosine kinase receptorsPhosphorylated ShcPTB domainRas-mitogenHomology 2Phosphorylation resultsPhosphotyrosyl peptidesProtein kinaseTyrosine phosphorylationBinding domainsSubsequent phosphorylationPhosphotyrosyl residuesShc
2008
Domain-oriented reduction of rule-based network models.
Borisov N, Chistopolsky A, Faeder J, Kholodenko B. Domain-oriented reduction of rule-based network models. IET Systems Biology 2008, 2: 342-51. PMID: 19045829, PMCID: PMC2628550, DOI: 10.1049/iet-syb:20070081.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsComputer SimulationGene Expression RegulationMembrane ProteinsModels, BiologicalProteomeSignal TransductionConceptsMulti-domain proteinsAuxiliary proteinsMembrane-bound receptorsTranscriptional regulatorsProgenitor proteinsProtein interactionsComplex assemblyGrowth factor receptorProteinFactor receptorSpeciesCorrect mass balanceEffector functionsHeterodimerisationReceptorsSitesRegulatorAssemblyInteractionDomain
2006
Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach.
Yalamanchili N, Zak D, Ogunnaike B, Schwaber J, Kriete A, Kholodenko B. Quantifying gene network connectivity in silico: scalability and accuracy of a modular approach. IET Systems Biology 2006, 153: 236-46. PMID: 16986625, PMCID: PMC2346590, DOI: 10.1049/ip-syb:20050090.Peer-Reviewed Original Research
2005
Signaling through Receptors and Scaffolds: Independent Interactions Reduce Combinatorial Complexity
Borisov N, Markevich N, Hoek J, Kholodenko B. Signaling through Receptors and Scaffolds: Independent Interactions Reduce Combinatorial Complexity. Biophysical Journal 2005, 89: 951-966. PMID: 15923229, PMCID: PMC1366644, DOI: 10.1529/biophysj.105.060533.Peer-Reviewed Original ResearchConceptsProtein complexesComplex signaling networksDistinct physiological responsesSignaling networksAdaptor proteinDocking siteMolecular eventsTemporal dynamicsPhysiological responsesDistinct sitesIndependent interactionsBranched networkSeparate domainsMolecular speciesDomain-oriented approachCombinatorial increaseReceptorsIndividual sitesSitesComplexesScaffoldsSpeciesTens of thousandsProteinDifferent sites
2004
Quantitative analysis of signaling networks
Sauro H, Kholodenko B. Quantitative analysis of signaling networks. Progress In Biophysics And Molecular Biology 2004, 86: 5-43. PMID: 15261524, DOI: 10.1016/j.pbiomolbio.2004.03.002.Peer-Reviewed Original ResearchSignal processing at the Ras circuit: what shapes Ras activation patterns?
Markevich NI, Moehren G, Demin OV, Kiyatkin A, Hoek JB, Kholodenko BN. Signal processing at the Ras circuit: what shapes Ras activation patterns? IET Systems Biology 2004, 1: 104-13. PMID: 17052120, DOI: 10.1049/sb:20045003.Peer-Reviewed Original ResearchConceptsP190 RhoGAPEpidermal growth factorActive GTP-bound stateGDP/GTP exchange factorGTP-bound stateSmall GTPase RasCellular signal transductionGTP exchange factorSystems biology approachSoluble tyrosine kinaseReceptor-mediated recruitmentSOS activationRasGAP activityRas proteinsCell fateExchange factorGTPase RasBiology approachRas mutantsSignal transductionInhibitory phosphorylationGTPase activityPlasma membraneRasGAPRegulatory mechanismsInferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data
Sontag E, Kiyatkin A, Kholodenko BN. Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics 2004, 20: 1877-1886. PMID: 15037511, DOI: 10.1093/bioinformatics/bth173.Peer-Reviewed Original Research
1996
Control analysis of glycolytic oscillations
Bier M, Teusink B, Kholodenko B, Westerhoff H. Control analysis of glycolytic oscillations. Biophysical Chemistry 1996, 62: 15-24. PMID: 8962468, DOI: 10.1016/s0301-4622(96)02195-3.Peer-Reviewed Original Research