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Computational Biology at Carnegie Mellon University A Quick Tour. Jaime Carbonell Carnegie Mellon University December, 2008. Computational Biology at CMU: Educational History. 1987 Undergraduate program in Computational Biology established

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computational biology at carnegie mellon university a quick tour

Computational Biology at Carnegie Mellon UniversityA Quick Tour

Jaime Carbonell

Carnegie Mellon University

December, 2008

computational biology at cmu educational history
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
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, …
curriculum for comp bio phd
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
Curriculum
  • Elective Courses
    • Computational Genomics
    • Computational Structural Biology
    • Cellular and Systems Modeling
    • Bioimage Informatics
    • Computational Neurobiology
    • Advanced Statistical Learning Methods
teaching advising faculty
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
Faculty: Computational Genomics

Linguistics methods for elucidating sequence-structure-function relations

  • Ziv Bar-Joseph*
  • Jaime Carbonell
  • Marie Dannie Durand*
  • Jonathan Minden
  • Ramamoorthi Ravi
  • Kathryn Roeder
  • Roni Rosenfeld
  • Larry Wasserman
  • Eric Xing*

Machine Learning methods for annotation

Modeling genome evolution through duplication

* = Primary research area

faculty computational structural biology proteomics
Faculty: Computational Structural Biology (Proteomics)

Homologous structure determination by NMR

  • Michael Erdmann
  • Maria Kurnikova*
  • Chris Langmead*
  • John Nagle
  • Gordon Rule
  • Robert Swendsen
  • Jaime Carbonell*

Improving determination of protein structure and dynamics using sparse data

Molecular dynamics of proteins and nucleic acids

faculty cellular and systems modeling
Faculty: Cellular and Systems Modeling

Computational modeling of mechanical properties of cells and tissues

  • Ziv Bar-Joseph*
  • Omar Ghattas
  • Philip LeDuc
  • Russell Schwartz*
  • Joel Stiles*
  • Shlomo Ta’asan
  • Yiming Yang
  • Eric Xing

Modeling of formation of protein complexes

Multi-scale modeling of excitable membranes

Discovery of large-scale gene regulatory networks

faculty bioimage informatics
Faculty: Bioimage Informatics

Determining subcellular location from microscope images

  • William Cohen
  • Bill Eddy
  • Christos Faloutsos
  • Jelena Kovacevic
  • Tom Mitchell*
  • Robert Murphy*
  • Eric Xing

Generative models of protein traffic

Machine learning of patterns of brain activity

Statistical analysis of gel images for proteomics

faculty computational neurobiology
Faculty: Computational Neurobiology

Development of structure of neuronal circuits

  • Justin Crowley
  • Tom Mitchell
  • Joel Stiles*
  • David Touretzky*
  • Nathan Urban

Machine learning of patterns of brain activity

Multi-scale modeling of excitable membranes

proteomics
Proteomics
  • Things to learn about proteins
    • sequence
    • activity
    • Partners
    • Structure
    • Functions
    • Expression level
    • Location/motility
examples of cool research
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

Human

Monkey

Mouse

Rat

Cow

Dog

Fly

Worm

Yeast

Evolutionary Methods for Discovering Sequence  Function Mapping (Rosenfeld)

A Multiple Sequence Alignment

Distribution of amino acids

Conserved Properties across Rhodopsin

subtask identifying chemical properties conserved at each protein position
Subtask: Identifying Chemical Properties Conserved at each Protein Position

A Single Position Results for All Rhodopsin Positions

new field location proteomics langmead
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
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
slide22

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
New Challenge: Functional Genomics
  • The various genome projectshave 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.
classical analysis of transcription regulation interactions
Classical Analysis of Transcription Regulation Interactions

“Gel shift”: electorphoretic mobility shift assay (“EMSA”) for DNA-binding proteins

*

Protein-DNA complex

*

Free DNA probe

Advantage: sensitive Disadvantage: requires stable complex;

little “structural” information about which

protein is binding

modern analysis of transcription regulation interactions
Modern Analysis of Transcription Regulation Interactions

Genome-wide Location Analysis

Advantage: High throughput Disadvantage: Inaccurate

gene regulation and carcinogenesis

oncogenetic

stimuli

(ie. Ras)

p14

extracellular

stimuli

(TGF-b)

cell damage

time required for DNA repair

severe DNA damage

p53

p53

activates

activates

G0 or G1

M

G2

activates

B

p16

A

PCNA

Promotes

cyclins D1,2 3

transcriptional activation

p15

p21

S

G1

E

any phase

E2F

Cdk

Apoptosis

Inhibits

Rb

Rb

-

+

Cyclin

TNF

TGF-b

...

Fas

PCNA (not cycle specific)

Phosphorylation of

+

PCNA

P

DNA repair

Gadd45

Gene Regulation and Carcinogenesis

Cancer !

the pathogenesis of cancer
The Pathogenesis of Cancer

Normal

BCH

DYS

CIS

SCC