From Sequence to Expression:A Probabilistic FrameworkEran Segal (Stanford)Joint work with:Yoseph Barash (Hebrew U.)Itamar Simon (Whitehead Inst.)Nir Friedman (Hebrew U.)Daphne Koller (Stanford)

Understanding Cellular ProcessesComplex biological processes (e.g. cell cycle)Coordination of multiple eventsEach event requires different modulesCan we recover the regulatory circuits that control such processes?

Gene Structure

Gene RegulationAmRNA

Gene RegulationAmRNA

Gene RegulationSwi5mRNA

Gene RegulationSwi5mRNA

Gene RegulationmRNA

GoalACTAGTGCTGACTATTATTGCACTGATGCTAGC+

Model of Gene RegulationGeneExperimentExpressionSequenceProbabilistic Relational Models (PRMs)Pfeffer and Koller (1998) Friedman et al (1999) Segal et al (2001)Promoter sequencesRegulation by transcription factorsExpression measurements Context Cluster

Regulation to ExpressionLevelGeneExperimentExpressionR(t1)R(t2)Exp. typeR(t1) = yes t1 regulates gene R(t1) = no t1 does not regulate geneExp. cluster

Regulation to ExpressionLevelGeneExperimentExpressionR(t1)R(t2)Exp. typeExp. cluster

Modeling Context SpecificityLevelGeneExperimentExpressionR(t1)Exp. typeExp. clusterR(t2)

Sequence ModelLevelGeneExperimentExpressionR(t1)R(t2)Exp. typeSequenceAssumptions: Binding site is of length kBinding may occur at any k-merTF regulates gene if binding occurs anywhereExp. cluster

From Sequence to Regulation Assumptions: Binding site is of length kBinding may occur at any k-merTF regulates gene if binding occurs anywhere

From Sequence to Regulation Model for one gene g, promoter region of length 5 and k=2

Joint Probabilistic ModelLevelGeneExperimentExpressionR(t1)R(t2)Exp. typeExp. Clusterk-mers1skB(t1)B(t2)

Localization Assay

Localization AssaySwi5Induce TF protein level

Localization AssaySwi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5 TF binds to targetsInduce TF protein level

Localization Assay

Measure TF binding to promoter of every geneAssign confidence for each bindingSwi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Swi5Gene BoundGene Not Bound TF binds to targetsInduce TF protein level

Localization AssaySimon et al (2001)Localization data: measure TF binding to promoter of each gene (assign binding confidence)

Is Regulation Observed?Not quite

Localization is measured for specific conditionsLocalization is measured for large DNA regionsLocalization is noisy

Incorporating LocalizationLevelGeneExperimentExpressionR(t1)R(t2)Exp. typeExp. ClusterL(t1)L(t2)Observed localizationLocalization p-value is noisy sensor of actual regulationIf regulation occurs, p-value likely to be lowIf no regulation, p-value likely to be high

Localization ModelGeneR(t1)L(t1)Localization p-value is noisy sensor of actual regulationIf regulation occurs, p-value likely to be lowIf no regulation, p-value likely to be high Observed

Joint Probabilistic ModelLevelGeneExperimentExpressionR(t1)R(t2)Exp. typeExp. Clusterpromoters1skL(t1)L(t2)

Learning the ModelsExperimental DetailsLocalization Data

Learning the ModelsExperimental DetailsLocalization Data

Model LearningStructure Learning:Tree structureMissing Data:Experiment clusterRegulation variablesMotif Model:Parameter estimation

Model LearningGeneExpressionR(t2)R(t1)ExperimentExp. typeLevelpromoters1skExp. clusterL(t1)L(t1)

Resulting Bayesian NetworkLevel1,2R(t2)1R(t1)1Exp. typeExp. type2Level1,1Level2,2R(t2)2R(t1)2Level2,1Level3,2R(t2)3R(t1)3Level3,1L(t2)1L(t1)1L(t2)2L(t1)2L(t2)3L(t1)3s11sk1s12sk2s13sk3Exp. clusterExp. cluster

Model Learning: E-StepLevel1,2R(t2)1R(t1)1Exp. typeExp. type2Level1,1Level2,2R(t2)2R(t1)2Level2,1Level3,2R(t2)3R(t1)3Level3,1L(t2)1L(t1)1L(t2)2L(t1)2L(t2)3L(t1)3s11sk1s12sk2s13sk3Exp. clusterExp. clusterLoopy belief propagation

Model Learning: M-StepLevel1,2R(t2)1R(t1)1Exp. typeExp. type2Level1,1Level2,2R(t2)2R(t1)2Level2,1Level3,2R(t2)3R(t1)3Level3,1L(t2)1L(t1)1L(t2)2L(t1)2L(t2)3L(t1)3s11sk1s12sk2s13sk3Exp. clusterExp. clusterStandard ML estimationConjugate Gradient

Experimental ResultsYeastCell Cycle expression data (Spellman et al)Localization data for 9 TFs (Simon et al)Yeast genome (promoters)

GeneralizationLevelGeneExpressionR(t1)R(t2)ExperimentExp. ClusterGene log-likelihood-112.24

GeneralizationLevelGeneExpressionL(t1)L(t2)ExperimentExp. typeGene log-likelihoodLocalization-121.48-112.24

GeneralizationLevelGeneExpressionR(t1)R(t2)ExperimentExp. typeExp. ClusterGene log-likelihood-112.24

GeneralizationLevelGeneExpressionR(t1)R(t2)ExperimentExp. typeExp. ClusterGene log-likelihood-112.24

Generating Hypotheses

Expression vs Regulation02142638410510701001301601902202500306090120150090180270360alphacdc15cdc28elu-1-0.500.51Genes predicted to be regulated by Swi5 are probably real Swi5 targets

Combinatorial Effects02142638410510701001301601902202500306090120150090180270360alphacdc15cdc28elu-1-0.500.51PhaseFkh2 & Swi4Fkh2 & Ndd1

Combinatorial Effects-1-0.500.5102142638410510701001301601902202500306090120150090180270360alphacdc15cdc28eluMcm1 & Ndd1Mcm1 & Ace2Mcm1 & Swi5Phase

Localization Assignment Changes

Motifs FoundNdd1Simon et al.Expanded SetRemaining Genes17128Expanded set identified additional genes regulated by Ndd1

TFSimonExpandedRestP-ValueAce210911.4e-6Fkh1292584.4e-10Fkh22929105.4e-11Mbp1665681.9e-45Mcm1282424.2e-18Ndd1172811.9e-24Swi4413756.4e-26Swi5282324.9e-15Swi6505262.3e-48

Induced Interaction NetworkTF pairs whose regulation predicts expression of same gene cluster Ace2Swi5Ndd1Fkh2Fkh1Swi4Swi6Mcm1Mbp1MG1G2S

ConclusionsUnified probabilistic model explaining gene regulation using sequence, localization and expression dataModels complex interactions between regulatorsDiscriminative model maximizing P(Expr. | Seq.)Sequence data helps explain expression patterns

Big PictureGoal: unified probabilistic frameworkModels complex biological domainsIncorporates heterogeneous dataFramework incorporates explicitly within model basic biological building blocks:Genes, TFs, proteins, patients, cells, species, Much closer connection between biology and modelCan read biology directly from modelCan incorporate prior knowledge easilyCan explicitly represent and learn biological models

This could be a model of the system: the expression level of a particular gene, in a specific context, defined by the experiment type, depends on whether its regulated and by which TFs. This regulation event, for each TF, depends in turn on the presence of the DNA binding domain for that TF in the upstream sequence of the geneSo, are we done? Well, not quite biologists dont know how to build this model. Instead