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Learning Regulatory Networks that Represent Regulator States and Roles Keith Noto ([email protected])...

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  • Learning Regulatory Networks that Represent Regulator States and Roles

    Keith Noto ([email protected]) and Mark Craven

    K. Noto and M. Craven, Learning Regulatory Network Models that Represent Regulator States and Roles. To appear in Lecture Notes in Bioinformatics.

  • TaskGiven:Gene expression dataOther sources of datae.g. sequence data, transcription factor binding sites, transcription unit predictionsDo:Construct a model that captures regulatory interactions in a cell

  • Key Ideas: States and RolesRegulator statesCannot be observedDepend on more than regulator expressionWe use cellular conditions as surrogates/predictors of regulation effectorsRegulator rolesIs a regulator an activator or a repressor?We use sequence analysis to predict these rolesEffectorCellularConditionRegulatorExpressionRegulateeExpressionRegulateeExpressionRegulatorState

  • Network Variables and StructureHidden Regulator States:activated or inactivatedCellular Conditions:stationary growth phase, heat shock, ...Regulatees: expression states represented as a mixture of GaussiansRegulators: expression states represented as a mixture of GaussiansConnect where we have evidence of regulationSelect relevant parents

  • Network Parameters: Hidden Nodes use CPD-TreesParents selected from regulator expression, cellular conditionsMay contain context-sensitive independenceGrowthMediumHeatShockmetJmetJstateGrowth Phase = Log PhaseGrowthPhaseGrowth PhasemetJmetJ = Low expression metJ Low expression Growth Phase Log

    P(metJ state = activated): 0.001

    P(metJ state = activated): 0.994

    P(metJ state = activated): 0.004

  • Initializing Roles0.6 0.40.2 0.80.9 0.10.5 0.5metA transcription unitTranscription Start Site*-35UpstreamDownstreamDNAmetRstatemetJstatemetAmetJ stateP(Low) P(High) activated activated activated inactivated inactivated activatedInactivated inactivated

    metR stateCPT for regulatee metABinding sites(metR binds upstream; considered an activator)(metJ binds downstream; considered a repressor)*Predicted transcription start sites from Bockhorst et. al., ISMB 03

  • Training the ModelInitialize the parametersActivators tend to bind more upstream than repressorsUse an EM algorithm to set parametersE-Step: Determine expected states of regulatorsM-Step: Update CPDsRepeat until convergence

  • Experimental Data and ProcedureExpression measurements from Affymetrix microarrays (Fred Blattners lab, University of Wisconsin-Madison)Regulator binding site predictions from TRANSFAC, EcoCyc, cross-species comparison (McCue, et. al., Genome Research 12, 2002)Experimental data consists of:90 Experiments6 Cellular condition variables (between two and seven values)296 regulatees64 regulatorsCross-fold validationMicroarrays held aside for testingConditions from test microarrays do not appear in training set

  • Log LikelihoodAverage Squared ErrorClassification Error Model-12,0040.5113.34%Our Model(3 iterations of adding missing TFs)-12,1930.5112.42%Baseline #2(No hidden nodes, using cellular conditions)-13,3630.7522.16%Baseline #1(No hidden nodes, no cellular conditions)-11,8930.5414.19%Random Initialization(3 iterations of adding missing TFs)

  • K. Noto and M. Craven, Learning Regulatory Network Models that Represent Regulator States and Roles. To appear in Lecture Notes in Bioinformatics.

    T.U. FOR metA: 4211812 -> 4212867 (FORWARD)

    Rbinding site->metR4211748 4211762

    metJ 4211808 4211815metJ4211816 4211823metJ4211824 4211831metJ4211832 4211839

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