Computational Biology at
Carnegie Mellon University
A Quick Tour
Jaime Carbonell
Carnegie Mellon University
December, 2008
Computational Biology at
CMU: Educational History
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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
Computational Biology at
CMU: History
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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, …
Joint CMU-Pitt Ph.D. Program
in Computational Biology
Curriculum for Comp Bio PhD
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Core graduate courses
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Molecular Biology
Biochemistry
Biophysics
Advanced Algorithms & Language Tech.
Machine Learning Methods
Computational Genomics
Computational Structural Biology
Cellular and Systems Modeling
Curriculum
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Elective Courses
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Computational Genomics
Computational Structural Biology
Cellular and Systems Modeling
Bioimage Informatics
Computational Neurobiology
Advanced Statistical Learning Methods
Example Books Used
Teaching & Advising Faculty
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30 faculty from CMU
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11 Computer Science
11.5 Biology and Chemistry
3.5 Bio-Engineering
3 Statistics and Mathematics
1 Business School
36 faculty from Pitt
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19 Medical School
17 Biology, Chemistry, Physics
Faculty: Computational
Genomics
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Ziv Bar-Joseph*
Jaime Carbonell
Marie Dannie Durand*
Jonathan Minden
Ramamoorthi Ravi
Kathryn Roeder
Roni Rosenfeld
Larry Wasserman
Eric Xing*
* = Primary research area
Linguistics methods
for elucidating
sequence-structurefunction relations
Machine Learning
methods for
annotation
Modeling genome
evolution through
duplication
Faculty: Computational
Structural Biology (Proteomics)
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Homologous
structure
determination by
NMR
Michael Erdmann
Maria Kurnikova*
Improving
Chris Langmead*
determination of
protein structure and
John Nagle
dynamics using
sparse data
Gordon Rule
Molecular dynamics of
proteins and nucleic
Robert Swendsen
acids
Jaime Carbonell*
Faculty: Cellular and Systems
Modeling
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Computational
modeling of
mechanical
properties of
cells and tissues
Ziv Bar-Joseph*
Omar Ghattas
Philip LeDuc
Modeling of
Russell Schwartz*
formation of
protein complexes
Joel Stiles*
Multi-scale modeling
Shlomo Ta’asan
of excitable
membranes
Yiming Yang
of large-scale gene
Eric Xing Discoveryregulatory
networks
Faculty: Bioimage Informatics
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Determining
subcellular location
from microscope
images
William Cohen
Bill Eddy
Christos Faloutsos Generative models of
protein traffic
Jelena Kovacevic
Machine learning of
patterns of brain activity
Tom Mitchell*
Robert Murphy*
Statistical analysis of gel
images for proteomics
Eric Xing
Faculty: Computational
Neurobiology
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Justin Crowley
Tom Mitchell
Joel Stiles*
David Touretzky*
Nathan Urban
Development of
structure of neuronal
circuits
Machine learning of
patterns of brain
activity
Multi-scale modeling of
excitable membranes
Proteomics
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Things to learn about proteins
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sequence
activity
Partners
Structure
Functions
Expression level
Location/motility
Examples of Cool Research
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Computational Biolinguistics
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Sequence (DNA, Protein)  Structure  Function
Language (Speech, Text)  Syntax  Semantics
GPCRs (sensor/channel proteins, Klein CMU/Pitt)
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Evolutionary Analysis (of genes, proteins, …)
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Conservation, replication, poly-functionality (Rosenberg)
Immune System Modeling (just starting…)
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60% of all targeted drugs affect GPCRs
Language (information-theoretic) analysis
Domain/Fold polymorphic modeling (Langmead)
Cross-species Interactome (just starting…)
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Human-HIV protein-protein (Carbonell, Klein)
Evolutionary Methods for Discovering
Sequence  Function Mapping (Rosenfeld)
A Multiple Sequence Alignment
Human
Monkey
Mouse
Rat
Cow
Dog
Fly
Worm
Yeast
Conserved Properties across Rhodopsin
Distribution of amino acids
Subtask: Identifying Chemical Properties
Conserved at each Protein Position
A Single Position
Results for All Rhodopsin Positions
Five Classifiers in Gene Identification for Cancer/H5 (Yang)
New Field:
Location Proteomics (Langmead)
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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)
Quaternary Fold Predictions
(Carbonell & Liu)
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Triple beta-spirals [van Raaij et al. Nature 1999]
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Virus fibers in adenovirus, reovirus and PRD1
Double barrel trimer [Benson et al, 2004]
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Coat protein of adenovirus, PRD1, STIV, PBCV
Model Organism: Bacterial Phage T4:
(Ultimate targets are HIV, etc.)
Dendritic Clustering for Clone (Murphy)
Protein name
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
New Challenge: Functional
Genomics
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The various genome
projects have yielded the
complete DNA sequences
of many organisms.
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E.g. human, mouse, yeast,
fruitfly, etc.
Human: 3 billion base-pairs,
30-40 thousand genes.
Challenge: go from
sequence to function,
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i.e., define the role of each
gene and understand how the
genome functions as a whole.
Classical Analysis of Transcription
Regulation Interactions
“Gel shift”: electorphoretic mobility shift assay
(“EMSA”) for DNA-binding proteins
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Protein-DNA complex
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Free DNA probe
Advantage: sensitive
Disadvantage: requires stable complex;
little “structural” information about which
protein is binding
Modern Analysis of Transcription
Regulation Interactions
Genome-wide Location Analysis
Advantage: High throughput
Disadvantage: Inaccurate
Gene Regulatory Network
Induction (Xing et al)
Gene Regulation and Carcinogenesis
oncogenetic
stimuli
(ie. Ras)
cell damage severe DNA damage
activates
time required for DNA repair
G2
M
G0 or G1
p53
p53
Promotes
p16
S
p21
G1
Inhibits
p15
p14
transcriptional
activation
activates
activates
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 Cancer !
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extracellular
stimuli
(TGF-b)
Cdk
Apoptosis
+
Phosphorylation of
Cycli
n
E2F
-
Fas
PCNA (not cycle
specific)
Rb
PCNA
Rb P
DNA repair
Gadd45
+
TNF
TGF-b
...
The Pathogenesis of Cancer
Normal
BCH
CIS
DYS
SCC