computational biology at carnegie mellon university a quick tour jaime carbonell carnegie mellon...
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Computational Biology at Carnegie Mellon University
A Quick Tour
Jaime CarbonellCarnegie Mellon University
December, 2008
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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
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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, …
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Joint CMU-Pitt Ph.D. Program in Computational Biology
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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
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Curriculum Elective Courses
Computational Genomics Computational Structural Biology Cellular and Systems Modeling Bioimage Informatics Computational Neurobiology Advanced Statistical Learning Methods
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Example Books Used
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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
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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
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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
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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
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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
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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
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Proteomics Things to learn about proteins
sequence activity Partners Structure Functions Expression level Location/motility
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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)
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Evolutionary Methods for Discovering Sequence Function Mapping (Rosenfeld)
HumanMonkeyMouseRatCowDogFlyWormYeast
A Multiple Sequence Alignment Distribution of amino acids
Conserved Properties across Rhodopsin
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Subtask: Identifying Chemical Properties Conserved at each Protein Position
A Single Position Results for All Rhodopsin Positions
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Five Classifiers in Gene Identification for Cancer/H5 (Yang)
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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)
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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
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Model Organism: Bacterial Phage T4: (Ultimate targets are HIV, etc.)
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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)
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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.
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Free DNA probe
*
*Protein-DNA complex
Advantage: sensitive Disadvantage: 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
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Modern Analysis of Transcription Regulation Interactions
Genome-wide Location Analysis
Advantage: High throughput Disadvantage: Inaccurate
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Gene Regulatory Network Induction (Xing et al)
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Gene Regulation and Carcinogenesis
PCNA (not cycle specific)
G0 or G1 M G2
S
G1
E
AB
+
PCNA
Gadd45DNA repair
Rb
E2F
Rb P
Cyclin
CdkPhosphorylation of
+ -
Apoptosis
FasTNF
TGF-...
p53
Pro
mo
tes
oncogeneticstimuli
(ie. Ras)
extracellularstimuli(TGF-)In
hibi
tsac
tiva
tes
acti
vate
s
p16
p15
p53
p14
tran
scrip
tiona
l ac
tivat
ion
p21
acti
vate
s
cell damagetime required for DNA repair severe DNA damage
Cancer !Cancer !
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Normal BCH
CIS
DYS
SCC
The Pathogenesis of Cancer