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
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
Systems biology‐embedded target validation: improving efficacy in drug discovery
Vandamme D, Minke BA, Fitzmaurice W, Kholodenko BN, Kolch W. Systems biology‐embedded target validation: improving efficacy in drug discovery. WIREs Mechanisms Of Disease 2013, 6: 1-11. PMID: 24214316, DOI: 10.1002/wsbm.1253.Peer-Reviewed Original ResearchConceptsSystems biology approachBiology approachDrug discoveryPotential drug targetsOmics technologiesMolecular mechanismsMultidrug treatmentDrug targetsMedical treatmentNovel targetDrug pipelineDrug developmentEfficacyAttrition ratesTreatmentDiscoveryGreater personalizationReductionist modelPatientsComplexity 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 outcomesSystems medicine: helping us understand the complexity of disease
Vandamme D, Fitzmaurice W, Kholodenko B, Kolch W. Systems medicine: helping us understand the complexity of disease. QJM 2013, 106: 891-895. PMID: 23904523, DOI: 10.1093/qjmed/hct163.Peer-Reviewed Original Research
2009
Four‐dimensional dynamics of MAPK information‐processing systems
Kholodenko BN, Birtwistle MR. Four‐dimensional dynamics of MAPK information‐processing systems. WIREs Mechanisms Of Disease 2009, 1: 28-44. PMID: 20182652, PMCID: PMC2826817, DOI: 10.1002/wsbm.16.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsHumansMammalsMAP Kinase Signaling SystemReceptors, Cell SurfaceSystems BiologyYeastsConceptsMultiple target proteinsMyriad of stimuliCell surface receptorsMAPK cascadeActive kinasePhosphorylation signalsProtein phosphorylationCellular outcomesCytosolic localizationProtein kinaseEndocytotic traffickingPlasma membraneProtein activityTarget proteinsMembrane confinementXenopus eggsToggle switchKinaseMolecular motorsIntracellular gradientsSpatio-temporal guidanceLarge cellsFour-dimensional dynamicsPhosphorylationTraffickingSystems‐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
2005
Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy
Andrec M, Kholodenko B, Levy R, Sontag E. Inference of signaling and gene regulatory networks by steady-state perturbation experiments: structure and accuracy. Journal Of Theoretical Biology 2005, 232: 427-441. PMID: 15572066, DOI: 10.1016/j.jtbi.2004.08.022.Peer-Reviewed Original ResearchThe International Consortium on Systems Biology of Receptor Tyrosine Kinase Regulatory Networks.
Sakaki Y, Kholodenko B, Hatakeyama M, Kitano H, Kolch W, De Meyts P, Yarden Y, Westerhoff H, Wiley H. The International Consortium on Systems Biology of Receptor Tyrosine Kinase Regulatory Networks. IET Systems Biology 2005, 152: 53-4. PMID: 17044231, DOI: 10.1049/ip-syb:20059002.Peer-Reviewed Original Research