Characterization of extrinsic variability in eukaryotic gene expression
Characterization of extrinsic variability in eukaryotic gene expression
批准号:
7333812
负责人:
Matthew R. Bennett
金额:
$4.68万
依托单位国家:
美国
项目类别:
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-09-01 至 2009-08-31
关键词:
AddressAffectBehaviorBiochemical ReactionBiological AssayBiologyBiomedical EngineeringCarrier ProteinsCell CycleCell VolumesCell physiologyCellsChemicalsComplexComputer AssistedComputer SimulationComputer softwareComputing MethodologiesConditionCulture MediaCytoplasmDataDaughterDevice DesignsDevicesDisciplineEnvironmentFacility Construction Funding CategoryFeedbackFluorescence MicroscopyFrequenciesGalactoseGene ExpressionGenesGenetic TranscriptionGoalsGrowthHandImageImaging TechniquesIndividualLaboratoriesLiteratureMeasuresMetabolismMicrofluidic MicrochipsMicroscopyModelingMolecular Biology TechniquesMothersNatureNoiseNumbersPersonal SatisfactionProductionProteinsRateRegulator GenesRegulatory PathwayRelative (related person)ReporterResearch PersonnelRunningSaccharomyces cerevisiaeSeriesSourceStandards of Weights and MeasuresStudentsSystemSystems BiologyTechniquesTimeTrainingTranscriptional RegulationWorkbasecellular imagingchemical reactioninterdisciplinary approachmathematical modelnetwork modelspromoterprotein degradationresearch studysimulationstem
中文摘要
描述(由申请人提供):本项目的目标是描述基因调控网络中存在的两个外在变异性的来源。转录和调控不断地受到外部变异性来源的影响,基因网络如何在这些条件下可靠地发挥作用是一个悬而未决的问题。这项建议的第一个具体目标是解决不同类型基因调控网络中细胞周期动态引起的可变性。随着细胞的生长和分裂,细胞体积的变化会导致调节通路中活跃的反应物的浓度发生变化。由于基础生化反应的速度取决于反应物的浓度,因此基因网络的动力学将受到细胞周期的影响。这项建议的第二个具体目标是检查基因网络中细胞间的可变性,当它们的转录被外部因素诱导时。许多基因网络可以通过在生长介质中引入化学诱导剂来打开。通常,一旦诱导物被引入,这些网络就会上调负责内化诱导物的运输蛋白。这种正反馈可以在引入诱导剂和网络完全激活之间的时间内产生较高的细胞间变异性。这些目标将通过涉及实验和计算方法的多学科方法来实现。实验方面将有三个方面。首先,将使用标准的分子生物学技术来创建所需的基因网络并添加荧光报告蛋白。其次,本实验室开发的现有微流控设备将用于控制细胞的生长条件,并使用荧光显微镜记录细胞数据。最后,从显微镜获得的荧光图像将与现有的细胞跟踪软件进行分析,以获得单个细胞的轨迹。计算建模将需要对所涉及的网络的综合模型进行离散随机模拟,并将用于描述和预测实验的行为。然后,实验结果将被用来生成更完整的模型,并对所涉及的网络有更一致的理解。系统生物学的主要目标之一是了解基因调控网络的动态。实现这一目标的一个障碍是对外在变异性的表征,以及对基因网络如何在嘈杂环境中可靠地发挥作用的理解。这项提案的具体目标将涉及转录调控的这些基本方面。
英文摘要
DESCRIPTION (provided by applicant): The goal of this project is to characterize two sources of extrinsic variability that are present in gene regulatory networks. Transcription and regulation are constantly being affected by extrinsic sources of variability, and it is an open question how gene networks reliably function under these conditions. The first specific aim of this proposal addresses the variability caused by cell cycle dynamics in different types of gene regulatory networks. As a cell grows and divides, the changes in the volume of the cell cause variability in the concentrations of the reactants that are active in the regulatory pathways. Since the rates of the underlying biochemical reactions depend on the concentrations of the reactants, the dynamics of gene networks will be affected by the cell cycle. The second specific aim of this proposal is the examination of the cell-to-cell variability in gene networks when their transcription is induced by an outside agent. Many gene networks can be turned on by introducing a chemical inducer into the growth media. Often, these networks up-regulate the transport protein responsible for the internalization of the inducer once it has been introduced. This positive feedback can create high cell-to-cell variability in the time between introduction of the inducer and the time at which the network is completely active. These aims will be addressed using a multidisciplinary approach involving both experimental and computational methods. The experimental aspects will be threefold. First, standard molecular biology techniques will be used to create the needed gene networks and to add fluorescent reporter proteins. Second, existing microfludic devices, developed in this lab, will be used to both control the growth conditions of cells and record cell data with the use of fluorescent microscopy. Finally, the fluorescent images obtained from the microscopy will be analyzed with existing cell tracking software to obtain trajectories of individual cells. The computational modeling will entail discrete stochastic simulations of comprehensive models of the networks involved, and will be used to both describe and predict the behavior of the experiments. The results of experiments will then be used, in turn, to generate more complete models and a more coherent understanding of the networks involved. One of the main goals of systems biology is to understand the dynamics of gene regulatory networks. One obstacle to this goal is the characterization of extrinsic variability, and the understanding of how gene networks reliably function in noisy environments. The specific aims of this proposal will address these fundamental aspects of transcriptional regulation.
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会议论文
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海外基金