1. Computational Biology at Carnegie Mellon University A Quick Tour Jaime Carbonell Carnegie Mellon University December, 2008

2. Computational Biology at CMU: Educational History 1987 Undergraduate program in Computational Biology established 1991 Howard Hughes Medical Institute grant to build undergrad curriculum 2000 M.S. Program in Computational Biology established 2005 Joint CMU & U. of Pittsburgh PHD Program in Computational Biology

3. Computational Biology at CMU: History 2002 NSF large ITR grant (CMU PI: Reddy & Carbonell) with U, Pitt, MIT, Boston U, NRC Canada Computational Biolinguistics 2003 NSF large ITR grant (CMU PI: Murphy) with UCSB, Berkeley, MIT Bioimage Informatics 2004-2008 10 small grants from NSF, NIH, Merck, Gates on: Computational proteomics, viral evolution, HIV-human interactome, …

4. Joint CMU-Pitt Ph.D. Program in Computational Biology

5. Curriculum for Comp Bio PhD Core graduate courses Molecular Biology Biochemistry Biophysics Advanced Algorithms & Language Tech. Machine Learning Methods Computational Genomics Computational Structural Biology Cellular and Systems Modeling

6. Curriculum Elective Courses Computational Genomics Computational Structural Biology Cellular and Systems Modeling Bioimage Informatics Computational Neurobiology Advanced Statistical Learning Methods

7. Example Books Used

8. Teaching & Advising Faculty 30 faculty from CMU 11 Computer Science 11.5 Biology and Chemistry 3.5 Bio-Engineering 3 Statistics and Mathematics 1 Business School 36 faculty from Pitt 19 Medical School 17 Biology, Chemistry, Physics

9. Faculty: Computational Genomics Ziv Bar-Joseph* Jaime Carbonell Marie Dannie Durand* Jonathan Minden Ramamoorthi Ravi Kathryn Roeder Roni Rosenfeld Larry Wasserman Eric Xing* Linguistics methods for elucidating sequence-structure- function relations Machine Learning methods for annotation Modeling genome evolution through duplication * = Primary research area

10. Faculty: Computational Structural Biology (Proteomics) Michael Erdmann Maria Kurnikova* Chris Langmead* John Nagle Gordon Rule Robert Swendsen Jaime Carbonell* Homologous structure determination by NMR Improving determination of protein structure and dynamics using sparse data Molecular dynamics of proteins and nucleic acids

11. Faculty: Cellular and Systems Modeling Ziv Bar-Joseph* Omar Ghattas Philip LeDuc Russell Schwartz* Joel Stiles* Shlomo Ta’asan Yiming Yang Eric Xing Computational modeling of mechanical properties of cells and tissues Modeling of formation of protein complexes Multi-scale modeling of excitable membranes Discovery of large-scale gene regulatory networks

12. Faculty: Bioimage Informatics William Cohen Bill Eddy Christos Faloutsos Jelena Kovacevic Tom Mitchell* Robert Murphy* Eric Xing Determining subcellular location from microscope images Machine learning of patterns of brain activity Statistical analysis of gel images for proteomics Generative models of protein traffic

13. Faculty: Computational Neurobiology Justin Crowley Tom Mitchell Joel Stiles* David Touretzky* Nathan Urban Multi-scale modeling of excitable membranes Machine learning of patterns of brain activity Development of structure of neuronal circuits

14. Proteomics Things to learn about proteins sequence activity Partners Structure Functions Expression level Location/motility

15. Examples of Cool Research Computational Biolinguistics Sequence (DNA, Protein)  Structure  Function Language (Speech, Text)  Syntax  Semantics GPCRs (sensor/channel proteins, Klein CMU/Pitt) 60% of all targeted drugs affect GPCRs Language (information-theoretic) analysis Evolutionary Analysis (of genes, proteins, …) Conservation, replication, poly-functionality (Rosenberg) Immune System Modeling (just starting…) Domain/Fold polymorphic modeling (Langmead) Cross-species Interactome (just starting…) Human-HIV protein-protein (Carbonell, Klein)

16. Evolutionary Methods for Discovering Sequence  Function Mapping (Rosenfeld) Human Monkey Mouse Rat Cow Dog Fly Worm Yeast A Multiple Sequence Alignment Distribution of amino acids Conserved Properties across Rhodopsin

17. Subtask: Identifying Chemical Properties Conserved at each Protein Position A Single Position Results for All Rhodopsin Positions

18. Five Classifiers in Gene Identification for Cancer/H5 (Yang)

19. New Field: Location Proteomics (Langmead) Can use CD-tagging (developed by Jonathan Jarvik and Peter Berget) to randomly tag many proteins Isolate separate clones, each of which produces one tagged protein Use RT-PCR to identify tagged gene in each clone Collect many live cell images for each clone using spinning disk confocal fluorescence microscopy Cluster proteins by their location patterns (automatically)

20. Quaternary Fold Predictions (Carbonell & Liu) Triple beta-spirals [van Raaij et al. Nature 1999] Virus fibers in adenovirus, reovirus and PRD1 Double barrel trimer [Benson et al, 2004] Coat protein of adenovirus, PRD1, STIV, PBCV

21. Model Organism: Bacterial Phage T4: (Ultimate targets are HIV, etc.)

22. Clone isolation and images collection by Jonathan Jarvik, CD-tagged gene identification by Peter Berget, Computational Analysis of patterns by Xiang Chen and Robert F. Murphy Protein name Dendritic Clustering for Clone (Murphy)

23. New Challenge: Functional Genomics The various genome projects have yielded the complete DNA sequences of many organisms. E.g. human, mouse, yeast, fruitfly, etc. Human: 3 billion base-pairs, 30-40 thousand genes. Challenge: go from sequence to function, i.e., define the role of each gene and understand how the genome functions as a whole.

24. Free DNA probe * * Protein-DNA complex Advantage: sensitiveDisadvantage: requires stable complex; little “structural” information about which protein is binding Classical Analysis of Transcription Regulation Interactions “Gel shift”: electorphoretic mobility shift assay (“EMSA”) for DNA-binding proteins

25. Modern Analysis of Transcription Regulation Interactions Genome-wide Location Analysis Advantage: High throughput Disadvantage: Inaccurate

26. Gene Regulatory Network Induction (Xing et al)

27. Gene Regulation and Carcinogenesis PCNA (not cycle specific) G 0 or G 1 M G2 G2 S G1 G1 E A B + PCNA Gadd45 DNA repair Rb E2F Rb P Cycli n Cdk Phosphorylation of + - Apoptosis Fas TNF TGF- ... p53 Promotes oncogenetic stimuli (ie. Ras) extracellular stimuli (TGF-  ) Inhibits activates p16 p15 p53 p14 transcriptional activation p21 activates cell damage time required for DNA repairsevere DNA damage      Cancer !  

28. NormalBCH CIS DYS SCC The Pathogenesis of Cancer