MECHANISMS AND MODELING OF NETWORKED CIRCADIAN PACEMAKER SYNCHRONIZATION
MECHANISMS AND MODELING OF NETWORKED CIRCADIAN PACEMAKER SYNCHRONIZATION
批准号:
8309142
负责人:
FRANCIS J DOYLE
金额:
$27.65万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-08-01 至 2015-07-31
关键词:
AdultBehaviorBilateralBiologicalBiological Neural NetworksBrainCell CommunicationCellsCircadian RhythmsClinicalCognitiveComputer SimulationComputing MethodologiesCoupledCouplingCuesDataDevelopmentEquationFetal DevelopmentGene ExpressionGraphHeterogeneityHormonesIndividualKineticsLengthMapsMeasurementMeasuresMediatingMetabolismMethodsMetricModelingMonitorMood DisordersNeuronsNoiseOutputPathway AnalysisPerformancePhasePhysiologyPreventionProcessPublic DomainsRegulationRelative (related person)ResolutionRoleSeriesSignal TransductionSleep DisordersSleep Wake CycleSolutionsSourceStatistical ModelsSystemTimebasecircadian pacemakerfetalhigh end computermodels and simulationnetwork modelsnovelprogramsresearch studyrestorationspatiotemporalstatisticssuprachiasmatic nucleustheoriestool
中文摘要
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英文摘要
DESCRIPTION (provided by applicant): The mammalian suprachiasmatic nucleus (SCN), required for daily cycles in behavior and physiology. How the cells of the SCN synchronize to coordinate behavior is largely unknown. We have established a collaborative program combining experimental and computational methods to study large numbers of circadian oscillators, their connections, and the real-time kinetics by which they self-synchronize and respond to perturbations in environmental timing cues. To understand circadian regulation within the brain, we must understand the topology and types of interactions between circadian neurons. Aim 1 will monitor the network of SCN oscillators as they synchronize during fetal development, during entrainment, following a phase shift, and after restoration of cell-cell communication in the adult SCN. Using novel wavelet-based time series analyses, we will estimate the strength and direction of individual connections in the SCN. Aim 2 will use graph theory and spatial statistics to quantify network features of the developing and adult SCN. These analyses will define the mechanisms of synchronization during normal development and following environmental perturbations and the relative contributions of local, regional or global coupling which contribute to period precision. Aim 3 will compare the performance of the SCN networks under the four conditions with both deterministic and stochastic model networks. The computational models will investigate the effects of intrinsic noise and cell-cell heterogeneity on circadian synchronization. Revealing how circadian oscillators interact to generate a coherent rhythmic output will have important clinical implications for prevention and treatment of circadian rhythm disruptions, including mood and sleep disorders.
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