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Le Song lesong@cs.cmu.edu Joint work with Mladen Kolar and Eric Xing KELLER: Estimating Time Evolving Interactions Between Genes
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2 Transient Biological Processes
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3 3 PPI Network
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4 Time-Varying Interactions
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5 The Big-Picture Questions What are the interactions? active What pathways are active at a particular time point and location? How will biological networks respond to stimuli (eg. heat shot)?
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6 Regulation of cell response to stimuli is paramount, but we can usually only measure (or compute) steady-state interactions
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7 … t=1 23T Current Practice Static Networks Microarray Time Series Dynamic Bayesian Networks
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8 Our Goal Reverse engineer temporal/spatial-specific “rewiring” gene networks Time t*t* n=1 --- what are the difficulties?
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9 Two Scenarios Smoothly evolving networks Abruptly changing networks
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10 Scenario I (This paper) Kernel reweighted L1-regularized logistic regression (KELLER) Key Idea I: reweighting observations Key Idea II: regularized neighborhood estimation
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11 Key Idea Weight temporally adjacent observations more than distal observations
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12 Key Idea Estimate the neighborhood of each gene separately via L1-regularized logistic regression Kernel Reweighting Log-likelihood L1-regularization
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13 Consistency Theorem 1: Under certain verifiable conditions (omitted here for simplicity), KELLER recovers the true topology of the networks:
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14 Synthetic data DBN and static networks do not benefit from more observations Number of Samples
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15 Key idea: Temporally Smoothing Tesla (Amr and Xing, PNAS 2009) TESLA: … Senario II
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16 Drosophila Life Cycle Larva Embryo Pupa Adult 66 microarrays across full life cycle 588 genes related to development
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17 molecular function biological process cellular component
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40 Network Size vs. Clustering Coefficient mid-embryonic mid-pupal
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41 Network Size vs. Clustering Coefficient mid-embryonic stage tight local clusters mid-pupal stage loose local clusters
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42 Interactivity of Gene Sets 27 genes based on ontology
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43 Interactivity of Gene Sets 25 genes based on ontology
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44 Transient Gene Interactions Time Gene Pairs Active Inactive msn dock sno Dl
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45 Transcriptional Factor Cascade Summary networks 36 transcription factors Node size its total activity
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46 TF Cascade – mid-embryonic stage
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47 TF Cascade – mid-larva stage
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48 TF Cascade – mid-pupal stage
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49 TF Cascade – mid-adult stage
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50 Transient Group Interactions
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51 Conclusion KELLER for reverse engineering “rewiring” networks Key advantages: Computationally efficient (scalable to 10 4 genes) Computationally efficient (scalable to 10 4 genes) Global optimal solution is attainable Global optimal solution is attainable Theoretical guarantee Theoretical guarantee Glimpse to temporal evolution of gene networks Many interactions are rewiring and transient Availability: http://www.sailing.cs.cmu.edu/
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52 The End Thanks Travel fellowship: Office of Science (BER), U.S. Department of Energy, Grant No. DE-FG02-06ED64270 Funding: Lane Fellowship, Questions?
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53 Interactivity of Gene Sets 30 genes based on ontology
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54 Timing of Regulatory Program Galactose
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55 Challenges Very small sample size Experimental data are scarce and costly Noisy measurement More genes than microarrays Complexity regularization needed to avoid over- fitting Observations no longer iid since the networks are changing!
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