1. Do not reproduce without permission 1 Gerstein.info/talks (c) 2004 1 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Permissions Statement This Presentation is copyright Mark Gerstein, Yale University, 2003, Feel free to use images in it with PROPER acknowledgement.

2. Do not reproduce without permission 2 Gerstein.info/talks (c) 2004 2 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Towards Protein Function: Relating mRNA Expression, Protein Abundance, and Protein-protein Interactions Mark B Gerstein Yale U Talk at Experimental Biology 2004 2004.04.17

3. Do not reproduce without permission 3 Gerstein.info/talks (c) 2004 3 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Understanding proteins, through analysis of populations rather than individuals

4. Do not reproduce without permission 4 Gerstein.info/talks (c) 2004 4 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu The central post- genomic term Greenbaum et al., Genome Res. 11:1463

5. Do not reproduce without permission 5 Gerstein.info/talks (c) 2004 5 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu The central post- genomic term Greenbaum et al., Genome Res. 11:1463

6. Do not reproduce without permission 6 Gerstein.info/talks (c) 2004 6 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu The central post- genomic term PubMed Hits Proteome Greenbaum et al., Genome Res. 11:1463

7. Do not reproduce without permission 7 Gerstein.info/talks (c) 2004 7 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Many Manifestations of Proteins & Research Topics in Proteomics Analyzing protein fossils (pseudogenes) in genomes Predicting protein function on a genomic scale Comparing folds & families between proteomes Analyzing protein flexibility in terms of packing Structures Sequences ArraysGels

8. Do not reproduce without permission 8 Gerstein.info/talks (c) 2004 8 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Towards Protein Function: Relating mRNA Expression, Protein Abundance, and Protein-protein Interactions 1.Relating mRNA expression & protein abundance: Correlation within broad categories and merge data 2.Relating mRNA expression & protein interactions (indirectly through abundance): Noisy Correlations in complexes 3.Predicting interactions through expression correlations integrated with other genomic information (reducing the noise) a.Results on yeast b.Intuition on Bayesian Methods

9. Do not reproduce without permission 9 Gerstein.info/talks (c) 2004 9 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Simple models connecting mRNA and Proteins where k s,i and k d,i are the protein synthesis and degradation rate constants At steady state: P i = k s;i [mRNA i ] k d,i = k s,i [ mRNA i ] – k d,i P i dPi dt

10. Do not reproduce without permission 10 Gerstein.info/talks (c) 2004 10 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Same mRNA levels yet protein data varied > 20X (N ~100, r = 0.9) Early Experiments: Gygi et al. MCB (1999)

11. Do not reproduce without permission 11 Gerstein.info/talks (c) 2004 11 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Same mRNA levels yet protein data varied > 20X Do some ORFs bias the results? 73 proteins (69%) R = 0.36

12. Do not reproduce without permission 12 Gerstein.info/talks (c) 2004 12 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Data Integration High-throughput data is less reliable than smaller-scale experiments. Merging reduces noise. Combining data increases accuracy & coverage

13. Do not reproduce without permission 13 Gerstein.info/talks (c) 2004 13 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Merged mRNA vs Protein (2D gels) r =0.67 Greenbaum et al Bioinformatics 2001

14. Do not reproduce without permission 14 Gerstein.info/talks (c) 2004 14 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Outliers (> 2 SD from the mean) High Protein Metabolism (1) Energy(2) MIPS [Mewes et al.] Low Protein Prot. Syn. (5) Prot. Fate (6)

15. Do not reproduce without permission 15 Gerstein.info/talks (c) 2004 15 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Merged mRNA vs Protein (2D gels + Mudpit) mRNA Set 6249 ORFsProtein Set # 2 2 2DE sets & 2 Mudpit ~2000 ORFs

16. Do not reproduce without permission 16 Gerstein.info/talks (c) 2004 16 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Functional Categories Co-regulated proteins

17. Do not reproduce without permission 17 Gerstein.info/talks (c) 2004 17 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Subcellular Localization

18. Do not reproduce without permission 18 Gerstein.info/talks (c) 2004 18 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Very Broad Categories: Enrichment of Amino Acids in Transcriptome and Translatome relative to Genome

19. Do not reproduce without permission 19 Gerstein.info/talks (c) 2004 19 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Changes in mRNA vs. protein Murine hematopoietic precursor MPRO change in expression 0 - 72 hr [S Weissmann]

20. Do not reproduce without permission 20 Gerstein.info/talks (c) 2004 20 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Changes in mRNA vs. protein Murine hematopoietic precursor MPRO change in expression 0 - 72 hr R = 0.58 ~ 80% of the genes are located in the first and third quadrants

21. Do not reproduce without permission 21 Gerstein.info/talks (c) 2004 21 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Changes in mRNA vs. protein Ratios of wt+gal to wt gal (ICAT vs microarray) Ideker et al Science, 2001 N ~ 290 r = 0.6

22. Do not reproduce without permission 22 Gerstein.info/talks (c) 2004 22 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Towards Protein Function: Relating mRNA Expression, Protein Abundance, and Protein-protein Interactions 1.Relating mRNA expression & protein abundance: Correlation within broad categories and merge data 2.Relating mRNA expression & protein interactions (indirectly through abundance): Noisy Correlations in complexes 3.Predicting interactions through expression correlations integrated with other genomic information (reducing the noise) a.Results on yeast b.Intuition on Bayesian Methods

23. Do not reproduce without permission 23 Gerstein.info/talks (c) 2004 23 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Expression & interactions Interactions form indirect relationship between mRNA expression & protein abundance  Proteins in a complex should occur in stoichiometric equal amounts and (apx) rise & fall in expression in unison Types of interactions  Protein complexes (e.g. proteasome, ribosome)  Aggregated interactions in vivo pull down (Ho, Gavin) yeast two-hybrid (Uetz, Ito)

24. Do not reproduce without permission 24 Gerstein.info/talks (c) 2004 24 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Interactions provide a way to define function Networks [Eisenberg et al.] Hierarchies & DAGs [Enzyme, Bairoch; GO, Ashburner; MIPS, Mewes, Frishman] Interaction Vectors [Lan et al, IEEE 90:1848]

25. Do not reproduce without permission 25 Gerstein.info/talks (c) 2004 25 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Relationship of Interactions to Abs. Expression Level

26. Do not reproduce without permission 26 Gerstein.info/talks (c) 2004 26 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Protein-Protein Interactions & Expression Correlations between selected expression timecourses (all pairs, control) (strong interactions in perm- anent complexes, clearly diff.) Cell Cycle CDC28 expt. (Davis) Sets of interactions (from MIPS) (Uetz et al.) Pairwise interactions

27. Do not reproduce without permission 27 Gerstein.info/talks (c) 2004 27 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Protein-Protein Interactions & Expression Correlations Sets of interactions between selected expression timecourses (all pairs, control) (from MIPS) (strong interactions in perm- anent complexes, clearly diff.) (Uetz et al.) Cell Cycle CDC28 expt. (Davis) Pairwise interactions

28. Do not reproduce without permission 28 Gerstein.info/talks (c) 2004 28 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu MCM3 MCM6 CDC47 MCM2 CDC46 CDC54 DPB3 CDC45 DPB2 CDC2 CDC7 POL2 HYS2 POL32 DBF4 ORC2 ORC6 ORC5 ORC4 ORC3 ORC1 MCM3 MCM6 CDC47 MCM2 CDC46 CDC54 DPB3 CDC45 DPB2 CDC2 CDC7 POL2 HYS2 POL32 DBF4 ORC2 ORC6 ORC5 ORC4 ORC3 ORC1 Representing Expression Correlations within a Large Complex in a Matrix

29. Do not reproduce without permission 29 Gerstein.info/talks (c) 2004 29 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu MCM3 MCM6 CDC47 MCM2 CDC46 CDC54 DPB3 CDC45 DPB2 CDC2 CDC7 POL2 HYS2 POL32 DBF4 ORC2 ORC6 ORC5 ORC4 ORC3 ORC1 MCM3 MCM6 CDC47 MCM2 CDC46 CDC54 DPB3 CDC45 DPB2 CDC2 CDC7 POL2 HYS2 POL32 DBF4 ORC2 ORC6 ORC5 ORC4 ORC3 ORC1 MCMs prots. ORC Polym.  &  Expression Correlations Segment Replication Complex into Component Parts

30. Do not reproduce without permission 30 Gerstein.info/talks (c) 2004 30 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Range of Expression Correlations within Complexes Replication Cplx Overall.05 ORC.19, MCMs.75 Pol. .45, .75, Ribosome Overall.80 Large.80 Small.81 Proteasome Overall.43 20S.50 19S.51

31. Do not reproduce without permission 31 Gerstein.info/talks (c) 2004 31 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Towards Protein Function: Relating mRNA Expression, Protein Abundance, and Protein-protein Interactions 1.Relating mRNA expression & protein abundance: Correlation within broad categories and merge data 2.Relating mRNA expression & protein interactions (indirectly through abundance): Noisy Correlations in complexes 3.Predicting interactions through expression correlations integrated with other genomic information (reducing the noise) a.Results on yeast b.Intuition on Bayesian Methods

32. Do not reproduce without permission 32 Gerstein.info/talks (c) 2004 32 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Predicting Interactions from Expression Analysis ~313K significant relationships from ~18M possible Correlated mRNA profiles predict possible interactions Very noisy

33. Do not reproduce without permission 33 Gerstein.info/talks (c) 2004 33 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Overview of information integrated and Bayesian Formalism Data suggestive of interactions (co-expression, co-localization, similar essentiality) Noisy high-throughput experiments (Gavin et al., Uetz et al. &c) Gold-standard complexes (MIPS, Mewes, Frishman et al.) Jansen et al. Science 302:449

34. Do not reproduce without permission 34 Gerstein.info/talks (c) 2004 34 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Overview of information integrated and Bayesian Formalism Cross-validated training and testing Thresholding L at various values Tabulation of observed TP and FP at various thresholds Jansen et al. Science (in press)Jansen et al. Science 302:449

35. Do not reproduce without permission 35 Gerstein.info/talks (c) 2004 35 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Comparison of network against gold standard complex interactions Positives 8250 known interactions in MIPS complexes [Mewes] Negatives ~2.7 M pairs in diff. Subcellular compartments TP FP Set of predicted “interactions” [Related Data in Bind, DIP]

36. Do not reproduce without permission 36 Gerstein.info/talks (c) 2004 36 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Observed TP/FP Ratio Tracks L, Suggesting a Threshold Jansen et al. Science 302:449

37. Do not reproduce without permission 37 Gerstein.info/talks (c) 2004 37 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Observed TP/FP Ratio Tracks L, Suggesting a Threshold Jansen et al. Science 302:449

38. Do not reproduce without permission 38 Gerstein.info/talks (c) 2004 38 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Observed TP/FP Ratio Tracks L, Suggesting a Threshold Jansen et al. Science 302:449

39. Do not reproduce without permission 39 Gerstein.info/talks (c) 2004 39 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Integration of Features Gives Much Higher Likelihood Ratios than Any Individual Feature Jansen et al. Science 302:449

40. Do not reproduce without permission 40 Gerstein.info/talks (c) 2004 40 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Comparison of Predictions with Known Complexes Jansen et al. Science 302:449

41. Do not reproduce without permission 41 Gerstein.info/talks (c) 2004 41 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Example prediction: Mito. Ribosome Jansen et al. Science 302:449

42. Do not reproduce without permission 42 Gerstein.info/talks (c) 2004 42 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Comparison with new experiments J Greenblatt RFA cplx Jansen et al. Science 302:449

43. Do not reproduce without permission 43 Gerstein.info/talks (c) 2004 43 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Towards Protein Function: Relating mRNA Expression, Protein Abundance, and Protein-protein Interactions 1.Relating mRNA expression & protein abundance: Correlation within broad categories and merge data 2.Relating mRNA expression & protein interactions (indirectly through abundance): Noisy Correlations in complexes 3.Predicting interactions through expression correlations integrated with other genomic information (reducing the noise) a.Results on yeast b.Intuition on Bayesian Methods

44. Do not reproduce without permission 44 Gerstein.info/talks (c) 2004 44 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Intuition for how to do Integration of Interactomes Diverse sources of interaction information  Databases (BIND, DIP, MIPS etc.) Individual expts. in literature  High-throughput datasets in vivo pull down (Ho, Gavin) yeast two-hybrid (Uetz, Ito)  Genomic data Expression Phenotypes Localization Functional Noisy  High-throughput data is less reliable than smaller scale experiments [Grigorev, Bork] Combining data increases  Accuracy & coverage [Church] How to do quantitatively?  How to weight different data sources?  General classification problem (machine learning) Bayesian Approaches….

45. Do not reproduce without permission 45 Gerstein.info/talks (c) 2004 45 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Example of data integration: RNA polymerase II Which subunits interact? Based on protein-protein interaction experiments [Kornberg] Compare with Gold Std. structure Edwards, Kus, et al. TIG 18:529

46. Do not reproduce without permission 46 Gerstein.info/talks (c) 2004 46 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Data integration: RNA polymerase II

47. Do not reproduce without permission 47 Gerstein.info/talks (c) 2004 47 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Data integration: RNA polymerase II

48. Do not reproduce without permission 48 Gerstein.info/talks (c) 2004 48 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Data integration: RNA polymerase II Interaction experiments before structure was known

49. Do not reproduce without permission 49 Gerstein.info/talks (c) 2004 49 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Data integration: RNA polymerase II

50. Do not reproduce without permission 50 Gerstein.info/talks (c) 2004 50 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Integrate using naive Bayes classifier Data integration: RNA polymerase II

51. Do not reproduce without permission 51 Gerstein.info/talks (c) 2004 51 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Data integration: RNA polymerase II Integrate using naive Bayes classifier

52. Do not reproduce without permission 52 Gerstein.info/talks (c) 2004 52 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Data integration: RNA polymerase II Integrate using naive Bayes classifier

53. Do not reproduce without permission 53 Gerstein.info/talks (c) 2004 53 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Weighted Voting: the Likelihood Ratio Vote: +2 = 1 + 1 + -1 + -1 + 1 + 1 With weights: likelihood ratio L = L 1 + L 2 + L 3 …

54. Do not reproduce without permission 54 Gerstein.info/talks (c) 2004 54 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Correlations between similar features

55. Do not reproduce without permission 55 Gerstein.info/talks (c) 2004 55 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Relative quality of different expts. L = (TP/FP) (N/P) [for uncorrelated features]

56. Do not reproduce without permission 56 Gerstein.info/talks (c) 2004 56 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Towards Protein Function: Relating mRNA Expression, Protein Abundance, and Protein-protein Interactions 1.Relating mRNA expression & protein abundance: Correlation within broad categories and merge data 2.Relating mRNA expression & protein interactions (indirectly through abundance): Noisy Correlations in complexes 3.Predicting interactions through expression correlations integrated with other genomic information (reducing the noise) a.Results on yeast b.Intuition on Bayesian Methods

57. Do not reproduce without permission 57 Gerstein.info/talks (c) 2004 57 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Acknowledgements Protein Function Prediction (Networks.GersteinLab.org) J Qian, R Jansen, A Drawid, C Wilson, H Yu, D Greenbaum, J Lin, N Luscombe, H Hegyi, Y Kluger Pseudogenes (Pseudogene.org) P Harrison, Z Zhang, Y Liu, S Balasubramanian, P Bertone, T Johnson, J Karro Macromolecular Motions (MolMovDB.org) J Junker, H Yu, N Echols, V Alexandrov, W Krebs, D Milburn, U Lehnert Collaborators J Chang, R Basri, J Greenblatt (N Krogan) Yale CEGS M Snyder (A Kumar, H Zhu, M Bilgin …) S Weissmann, P Miller (K Cheung) NESG.org G Montelione, A Edwards (B Kuss) NIH, NSF Structural Proteomics (PartsList.org) C Goh, N Lan, H Hegyi, R Das, S Douglas, B Stenger

58. Do not reproduce without permission 58 Gerstein.info/talks (c) 2004 58 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Web-based data analysis system with graphical interface Data processing Web-based data analysis system with graphical interface Data processing Data quality Web-based data analysis system with graphical interface Data processing Data quality Differential hybridisation scoring Web-based data analysis system with graphical interface Data processing Data quality Differential hybridisation scoring Graphical interface allows visualisation of data and comparison with original slides Slide level Web-based data analysis system with graphical interface Data processing Data quality Differential hybridisation scoring Graphical interface allows visualisation of data and comparison with original slides Slide level Spot level

59. Do not reproduce without permission 59 Gerstein.info/talks (c) 2004 59 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Examples: SIM: Example Shifted Relationship (SIM)

60. Do not reproduce without permission 60 Gerstein.info/talks (c) 2004 60 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu MIM: Examples: Example Shifted Relationship (MIM)

61. Do not reproduce without permission 61 Gerstein.info/talks (c) 2004 61 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Ratios of wt+gal to wt gal ICAT vs microarray N ~ 290, r = 0.6 Ideker et al Science, 2001

62. Do not reproduce without permission 62 Gerstein.info/talks (c) 2004 62 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu murine hematopoietic precursor MPRO change in expression 0 - 72 hr

63. Do not reproduce without permission 63 Gerstein.info/talks (c) 2004 63 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Outliers Generally… Alcohol dehydrogenase is also a stress induced protein in many organisms (Matton et al. 1990; An et al. 1991; Millar et al. 1994), Faster Ramp Up? Alternatively, it is possible to look into mRNA stability as a factor Presently there are many structures within mRNA that are thought to influence stability including, among others, stem loops, UTRs premature stops and uORFS (Klaff et al. 1996) MIPS fxns AA metabolism & Energy are 2X as likely to have high protein vs mRNA than the general population MIPS fxns Protein synthesis and Protein fate (folding, modification, destination) are more likely to have low protein vs mRNA than the general population

64. Do not reproduce without permission 64 Gerstein.info/talks (c) 2004 64 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu murine hematopoietic precursor MPRO change in expression 0 - 72 hr R = 0.58 ~ 80% of the genes are located in the first and third quadrants

65. Do not reproduce without permission 65 Gerstein.info/talks (c) 2004 65 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Understanding Protein Function on a Genomic Scale Originally, 250 of 650 known on chr. 22 [Dunham et al.] >>30K+ Proteins in Entire Human Genome (with alt. splicing).…… ~650

66. Do not reproduce without permission 66 Gerstein.info/talks (c) 2004 66 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Issues in defining protein function on a genomic scale Multi-functionality: 2 functions/protein (also 2 proteins/function) Role Conflation: molecular, cellular, phenotypic Fun terms… but do they scale?....

67. Do not reproduce without permission 67 Gerstein.info/talks (c) 2004 67 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Names in Biology: Systematic? Yippee  named for the reaction of a graduate student upon cloning protein. If she has a good result, she would write "yippee" in the margin of her notebook vulcan & klingon stranded at second  mutant dies during development, usually in 2nd larval stage sarah  affects female fertility (biblical ref.) Sonic & kryptonite Darkener of apricot & suppressor of white apricot ROP vs ROM  "Regulator of Copy Number" or RNA-I- II-complex-binding-protein Barentsz  named for Dutch explorer who froze to death near the North Pole. The mutant blocks the movement of a key mRNA, causing it to get stuck in wrong place Agoraphobic  mutant for which the larvae look normal but never crawl out of the egg single-minded Redtape  series of designations given to genes which, when mutated, block transport along axons. Lush & cheapdate  former wants alcohol, later makes susceptible [Adapted from conversations + Am. Sci.]

68. Do not reproduce without permission 68 Gerstein.info/talks (c) 2004 68 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Issues in defining protein function on a genomic scale Fun terms… but do they scale? Starry night (P Adler, ’94) For now, definable aspects of function: interactions, location, enzymatic rxn. [Babbit]

69. Do not reproduce without permission 69 Gerstein.info/talks (c) 2004 69 (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Predicting Protein Function on a Genome Scale Relating microarray experiments to protein abundance  Local Clustering (to identify time-shifted and inverted relationships)  Relating Clustering to Known Regulatory Relationships Relating them to protein interactions  Bayesian methods to uniformly & optimally combine evidence (in application to integration of protein interaction data)  Predicting interactions in yeast de novo from non-interaction data sources (with verification)