Featured Publications
Control of cell state transitions
Rukhlenko O, Halasz M, Rauch N, Zhernovkov V, Prince T, Wynne K, Maher S, Kashdan E, MacLeod K, Carragher N, Kolch W, Kholodenko B. Control of cell state transitions. Nature 2022, 609: 975-985. PMID: 36104561, PMCID: PMC9644236, DOI: 10.1038/s41586-022-05194-y.Peer-Reviewed Original ResearchMeSH KeywordsCell DifferentiationCell ProliferationDatasets as TopicModels, BiologicalPhenotypeSignal TransductionSingle-Cell AnalysisWorkflowConceptsCell state transitionsCell fateCell statesCell fate transitionsCell fate decisionsSingle-cell dataNew biological insightsFate transitionsMovement of cellsFate decisionsWaddington landscapePhenotypic dataBiological insightsOmics datasetsOmics dataCellular modelMechanistic modelLandscape1FateCellsDevelopment pathwaysLandscapeBiologyState transitionsTherapeutic interventionsA systematic analysis of signaling reactivation and drug resistance
Kholodenko B, Rauch N, Kolch W, Rukhlenko O. A systematic analysis of signaling reactivation and drug resistance. Cell Reports 2021, 35: 109157. PMID: 34038718, PMCID: PMC8202068, DOI: 10.1016/j.celrep.2021.109157.Peer-Reviewed Original ResearchUntangling the wires: A strategy to trace functional interactions in signaling and gene networks
Kholodenko BN, Kiyatkin A, Bruggeman FJ, Sontag E, Westerhoff HV, Hoek JB. Untangling the wires: A strategy to trace functional interactions in signaling and gene networks. Proceedings Of The National Academy Of Sciences Of The United States Of America 2002, 99: 12841-12846. PMID: 12242336, PMCID: PMC130547, DOI: 10.1073/pnas.192442699.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsMAP Kinase Signaling SystemModels, BiologicalModels, TheoreticalProtein BindingProtein KinasesSignal TransductionConceptsGene networksFunctional interactionMitogen-activated protein kinase cascadeProtein kinase cascadeProteomic data setsKinase cascadeCellular signalingLarge genomicsUnidentified elementsMechanistic levelCellular networkingSignalingCell systemGenomicsInteractionInteraction routesCascadeComputer-generated responsesNetwork responseCurrent methodologiesResponse
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
2020
Systems biology approaches to macromolecules: the role of dynamic protein assemblies in information processing
Rukhlenko O, Kholodenko B, Kolch W. Systems biology approaches to macromolecules: the role of dynamic protein assemblies in information processing. Current Opinion In Structural Biology 2020, 67: 61-68. PMID: 33126139, PMCID: PMC8062579, DOI: 10.1016/j.sbi.2020.09.007.Peer-Reviewed Original ResearchMeSH KeywordsHumansMacromolecular SubstancesMAP Kinase Signaling SystemNeoplasmsSignal TransductionSystems BiologyConceptsProtein assembliesProtein complex dynamicsDynamic protein assembliesMacromolecular protein assembliesSignal transduction networksSystems biology approachMEK-ERK pathwayProtein complexesTransduction networksCellular processesBiology approachMolecular machinesRAS-RAFOncogenic mutationsStructural studiesDrug resistanceTemporal dynamicsAssemblyRecent progressTumorigenesisMutationsPicture highlightsFine tuningPathwayDynamic process
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 strategyNoiseDissecting RAF Inhibitor Resistance by Structure-based Modeling Reveals Ways to Overcome Oncogenic RAS Signaling
Rukhlenko OS, Khorsand F, Krstic A, Rozanc J, Alexopoulos LG, Rauch N, Erickson KE, Hlavacek WS, Posner RG, Gómez-Coca S, Rosta E, Fitzgibbon C, Matallanas D, Rauch J, Kolch W, Kholodenko BN. Dissecting RAF Inhibitor Resistance by Structure-based Modeling Reveals Ways to Overcome Oncogenic RAS Signaling. Cell Systems 2018, 7: 161-179.e14. PMID: 30007540, PMCID: PMC6149545, DOI: 10.1016/j.cels.2018.06.002.Peer-Reviewed Original ResearchConceptsOncogenic RASERK signalingRAS/ERK pathwayRAF inhibitorsOncogenic Ras signalingMEK/ERKStructure-based modelingRAF inhibitor resistanceRAS mutant tumorsRas signalingPosttranslational modificationsRaf kinaseERK activityRAF dimerizationDrug-protein interactionsERK pathwayMultiple inhibitorsColony formationSignalingMutant NRASCell proliferationDrug designParadoxical activationInhibitor resistanceMechanistic dynamic model
2016
Three-factor models versus time series models: quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data
Frank TD, Kiyatkin A, Cheong A, Kholodenko BN. Three-factor models versus time series models: quantifying time-dependencies of interactions between stimuli in cell biology and psychobiology for short longitudinal data. Mathematical Medicine And Biology A Journal Of The IMA 2016, 34: 177-191. PMID: 27079221, DOI: 10.1093/imammb/dqw001.Peer-Reviewed Original ResearchConceptsBeta-adrenoceptor agonist clenbuterolGlucocorticoid receptor systemHuman embryonic kidney 293 cellsEmbryonic kidney 293 cellsAgonist clenbuterolTumor necrosisCritical time windowExtracellular signal-regulated kinases 1Mood disordersAntagonist drugsEpidermal growth factorAnimal studiesKidney 293 cellsCell responsesSignal-regulated kinases 1Behavioral levelGrowth factorCertain antagonistsLongitudinal dataERK activationHEK293 cellsKinase 1Cellular levelTime effectsTranscriptional activity
2015
Frequency modulation of ERK activation dynamics rewires cell fate
Ryu H, Chung M, Dobrzyński M, Fey D, Blum Y, Lee SS, Peter M, Kholodenko BN, Jeon NL, Pertz O. Frequency modulation of ERK activation dynamics rewires cell fate. Molecular Systems Biology 2015, 11: msb156458. PMID: 26613961, PMCID: PMC4670727, DOI: 10.15252/msb.20156458.Peer-Reviewed Original ResearchG Protein–Coupled Receptor Signaling Networks from a Systems Perspective
Roth S, Kholodenko B, Smit M, Bruggeman F. G Protein–Coupled Receptor Signaling Networks from a Systems Perspective. Molecular Pharmacology 2015, 88: 604-616. PMID: 26162865, DOI: 10.1124/mol.115.100057.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsFeedback, PhysiologicalHumansReceptors, G-Protein-CoupledSignal TransductionSystems BiologyConceptsG protein-coupled receptorsRole of GPCRsSignal transduction networksProtein-protein interactionsSystems biology studiesSystems biology researchProtein-coupled receptorsCell surface receptorsSignaling networksExtracellular signalsMammalian cellsSignaling routeSingle proteinIntracellular proteinsExternal cuesAdaptive responseBiophysical conceptsProteinReceptorsFeedforward circuitryCellsConformationResponseCues
2013
Complexity of Receptor Tyrosine Kinase Signal Processing
Volinsky N, Kholodenko BN. Complexity of Receptor Tyrosine Kinase Signal Processing. Cold Spring Harbor Perspectives In Biology 2013, 5: a009043. PMID: 23906711, PMCID: PMC3721286, DOI: 10.1101/cshperspect.a009043.Peer-Reviewed Original ResearchConceptsCombinatorial varietySignal processingMathematical modelingSpecific system propertiesSpecific cell fate decisionsSystem propertiesNetwork behaviorReceptor tyrosine kinasesMultiple feedsSpatiotemporal dynamicsComputational approachExcitable responseIntricate landscapeCell fate decisionsSpecific cellular outcomesPathway cross talkOscillationsBistabilityNetwork circuitryDynamicsModelingTranscriptional controlAdapts cellsCellular outcomes
2012
Emergence of bimodal cell population responses from the interplay between analog single-cell signaling and protein expression noise
Birtwistle MR, Rauch J, Kiyatkin A, Aksamitiene E, Dobrzyński M, Hoek JB, Kolch W, Ogunnaike BA, Kholodenko BN. Emergence of bimodal cell population responses from the interplay between analog single-cell signaling and protein expression noise. BMC Systems Biology 2012, 6: 109. PMID: 22920937, PMCID: PMC3484110, DOI: 10.1186/1752-0509-6-109.Peer-Reviewed Original ResearchConceptsProtein expression noiseSingle-cell signalingExtracellular signal-regulated kinaseExpression noiseCell variabilityEpidermal growth factor stimulationProtein abundance variationCell fate decisionsPopulation responsesGrowth factor stimulationSingle cellsSignal-regulated kinaseCell population levelProtein expressionSingle-cell levelERK pathway activationFate decisionsPopulation levelFactor stimulationCell signalingCell population responseERK responseBiological outcomesPathway activationCell level
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 ResearchSystems‐level interactions between insulin–EGF networks amplify mitogenic signaling
Borisov N, Aksamitiene E, Kiyatkin A, Legewie S, Berkhout J, Maiwald T, Kaimachnikov NP, Timmer J, Hoek JB, Kholodenko BN. Systems‐level interactions between insulin–EGF networks amplify mitogenic signaling. Molecular Systems Biology 2009, 5: msb200919. PMID: 19357636, PMCID: PMC2683723, DOI: 10.1038/msb.2009.19.Peer-Reviewed Original ResearchMeSH KeywordsAdaptor Proteins, Signal TransducingCell LineDose-Response Relationship, DrugDrug SynergismEnzyme ActivationEpidermal Growth FactorGRB2 Adaptor ProteinHumansImmunoprecipitationInsulinMitogen-Activated Protein KinasesMitogensModels, BiologicalPhosphoinositide-3 Kinase InhibitorsPhosphorylationProtein Kinase InhibitorsProtein Tyrosine Phosphatase, Non-Receptor Type 11Ras ProteinsReproducibility of ResultsSignal TransductionSrc-Family KinasesSystems BiologyConceptsInsulin receptor substrateEpidermal growth factorRas/ERK cascadeCrosstalk mechanismsComplex cellular responsesPhosphatase SHP2Mitogenic signalingERK cascadeSrc kinaseReceptor substrateERK activityRaf levelsInsulin-induced increaseERK activationCellular responsesGab1HEK293 cellsExternal cuesEGF dosesPoor activatorGrowth factorMitogenicMitogenic responseComputational approachSHP2
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 functionsHeterodimerisationReceptorsSitesRegulatorAssemblyInteractionDomainGiving Space to Cell Signaling
Kholodenko BN, Kolch W. Giving Space to Cell Signaling. Cell 2008, 133: 566-567. PMID: 18485861, DOI: 10.1016/j.cell.2008.04.033.Peer-Reviewed Original Research
2007
Ligand‐dependent responses of the ErbB signaling network: experimental and modeling analyses
Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN. Ligand‐dependent responses of the ErbB signaling network: experimental and modeling analyses. Molecular Systems Biology 2007, 3: msb4100188. PMID: 18004277, PMCID: PMC2132449, DOI: 10.1038/msb4100188.Peer-Reviewed Original ResearchMeSH KeywordsAndrostadienesButadienesCell Line, TumorCell MembraneDimerizationEnzyme ActivationEpidermal Growth FactorExtracellular Signal-Regulated MAP KinasesFeedback, PhysiologicalHumansLigandsModels, BiologicalNeuregulin-1NitrilesPhosphoinositide-3 Kinase InhibitorsPhosphorylationProtein Structure, TertiaryProto-Oncogene Proteins c-aktReceptor Protein-Tyrosine KinasesReproducibility of ResultsSignal TransductionWortmanninConceptsEpidermal growth factorERK activityEGF-induced signalingMultiple human cancersPhosphoinositol-3 kinaseLigand-dependent responsesSustained signalingERK activationDownstream proteinsAkt activationInhibitor U0126Major regulatorHuman cancersErbB receptorsLigand dosesHeregulinErbBKinaseSignalingGrowth factorActivationKey roleU0126AktRegulator
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