Computational Biology at Carnegie Mellon University

A Quick Tour

Jaime CarbonellCarnegie Mellon University

December, 2008

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

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, …

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

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

Curriculum Elective Courses

Computational Genomics Computational Structural Biology Cellular and Systems Modeling Bioimage Informatics Computational Neurobiology Advanced Statistical Learning Methods

Example Books Used

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

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

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

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

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

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

Proteomics Things to learn about proteins

sequence activity Partners Structure Functions Expression level Location/motility

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)

Evolutionary Methods for Discovering Sequence Function Mapping (Rosenfeld)

HumanMonkeyMouseRatCowDogFlyWormYeast

A Multiple Sequence Alignment Distribution of amino acids

Conserved Properties across Rhodopsin

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)

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)

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

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

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)

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.

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

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

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 !

Normal BCH

CIS

DYS

SCC

The Pathogenesis of Cancer