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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 l.jpg

Computational Biology at Carnegie Mellon UniversityA Quick Tour

Jaime Carbonell

Carnegie Mellon University

December, 2008


Computational biology at cmu educational history l.jpg
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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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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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

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


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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


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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


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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


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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


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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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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


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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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New Field: Protein PositionLocation 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 Protein Position(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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Dendritic Clustering for Clone (Murphy) HIV, etc.)

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


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New Challenge: Functional Genomics HIV, etc.)

  • 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.


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Classical Analysis of Transcription Regulation Interactions HIV, etc.)

“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


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Modern Analysis of Transcription Regulation Interactions HIV, etc.)

Genome-wide Location Analysis

Advantage: High throughput Disadvantage: Inaccurate


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Gene Regulatory Network Induction HIV, etc.)(Xing et al)


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oncogenetic HIV, etc.)

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 !


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The Pathogenesis of Cancer HIV, etc.)

Normal

BCH

DYS

CIS

SCC


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