examples of functional modeling n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Examples of functional modeling. PowerPoint Presentation
Download Presentation
Examples of functional modeling.

Loading in 2 Seconds...

play fullscreen
1 / 29

Examples of functional modeling. - PowerPoint PPT Presentation


  • 140 Views
  • Uploaded on

Examples of functional modeling. NCSU GO Workshop 29 October 2009. Tools and materials from this workshop will be available online at the AgBase database Educational Resources link. For continuing support and assistance please contact: agbase@cse.msstate.edu.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Examples of functional modeling.' - iolani


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
examples of functional modeling

Examples of functional modeling.

NCSU GO Workshop

29 October 2009

slide2
Tools and materials from this workshop will be available online at the AgBase database Educational Resources link.
  • For continuing support and assistance please contact:

agbase@cse.msstate.edu

This workshop is supported by USDA CSREES grant number MISV-329140.

slide6

"Today’s challenge is to realise greater knowledge and understandingfrom the data-rich opportunities provided by modern high-throughput genomic technology."Professor Andrew Cossins, Consortium for Post-Genome Science, Chairman.

bio ontologies
Bio-ontologies
  • Bio-ontologies are used to capture biological information in a way that can be read by both humans and computers.
    • necessary for high-throughput “omics” datasets
    • allows data sharing across databases
  • Objects in an ontology (eg. genes, cell types, tissue types, stages of development) are well defined.
  • The ontology shows how the objects relate to each other.
what is the gene ontology
What is the Gene Ontology?

“a controlled vocabulary that can be applied to all organisms even as knowledge of gene and protein roles in cells is accumulating and changing”

the de facto standard for functional annotation

assign functions to gene products at different levels, depending on how much is known about a gene product

is used for a diverse range of species

structured to be queried at different levels, eg:

find all the chicken gene products in the genome that are involved in signal transduction

zoom in on all the receptor tyrosine kinases

human readable GO function has a digital tag to allow computational analysis of large datasets

COMPUTATIONALLY AMENABLE ENCYCLOPEDIA OF GENE FUNCTIONS AND THEIR RELATIONSHIPS

use go for
Use GO for…….
  • Determining which classes of gene products are over-represented or under-represented.
  • Grouping gene products.
  • Relating a protein’s location to its function.
  • Focusing on particular biological pathways and functions (hypothesis-testing).
slide11

Translation to clinical research: Pig

Global mRNA and protein expression was measured

from quadruplicate samples of control, X- and Y-treated tissue.

Differentially-expressed mRNA’s and proteins identified from Affymetrix microarray data and DDF shotgun proteomics using Monte-Carlo resampling*.

* Nanduri, B.,P. Shah, M. Ramkumar, E. A. Allen, E. Swaitlo,S. C. Burgess*,and M. L. Lawrence*. 2008. Quantitative analysis of Streptococcus Pneumoniae TIGR4 response to in vitro iron restriction by 2-D LC ESI MS/MS. Proteomics 8, 2104-14.

Using network and pathway analysis as well as Gene Ontology-based hypothesis testing, differences in specific phyisological processes between X- and Y-treated were quantified and reported as net effects.

Bindu Nanduri

slide12

Proportional distribution of mRNA functions differentially-expressed by X- and Y-treated tissues

Treatment Y

Treatment X

immunity (primarily innate)

inflammation

Wound healing

Lipid metabolism

response to thermal injury

angiogenesis

Total differentially-expressed mRNAs: 1960

Total differentially-expressed mRNAs: 4302

slide13

Net functional distribution of differentially-expressed mRNAs: X- vs. Y-Treatment

Y

X

sensory response to pain

angiogenesis

response to thermal injury

Lipid metabolism

Wound healing

classical inflammation

(heat, redness, swelling, pain, loss of function)

immunity (primarily innate)

35

30

25

20

15

10

5

0

5

Relative bias

slide14

Proportional distribution of protein functions differentially-expressed by X- and Y-treated tissues

Treatment Y

Treatment X

immunity (primarily innate)

inflammation

Wound Healing

Lipid metabolism

response to Thermal Injury

Angiogenesis

hemorrhage

Total differentially-expressed proteins: 433

Total differentially-expressed proteins: 509

slide15

Net functional distribution of differentially-expressed Proteins: X- vs. Y-Treatment

hemorrhage

sensory response to pain

Treatment Y

Treatment X

angiogenesis

response to thermal injury

lipid metabolism

Wound healing

classical inflammation

(heat, redness, swelling, pain, loss of function)

immunity (primarily innate)

8

6

4

2

0

2

4

6

Relative bias

slide16

B-cells

Stroma

apoptosis

immune response

cell-cell signaling

(Looking at function, not gene.)

slide18

Focusing on particular biological pathways and functions (hypothesis-testing).

Shyamesh Kumar BVSc

slide19

a-CD30 mab

a-CD8 mab

Susceptible (L72)

Resistant ( L61)

The critical time point in MD lymphomagenesis

18

16

Genotype

14

Susceptible (L72)

Resistant (L61)

12

mean total lesion score

10

Non-MHC associated resistance and susceptibility

8

6

4

2

0

0

20

40

60

80

100

days post infection

Burgess et al,Vet Pathol 38:2,2001

slide20

Tissue

CD30hi, Neoplastically-transformed

CD30 lo/- hyperplastic

Marek’s Disease Lymphoma Model : Chicken

The neoplastically-transformed (CD30hi) cells in Marek’s disease lymphoma cell phenotype most closely resembles T-regulatory cells. LA Shack, T. Buza, SC Burgess. Cancer Immunology and Immunotherapy, 2008

slide21

Whole tissue mRNA expression

L6 (R)

*

*

*

L7 (S)

25

20

*

40 – mean Ct value

15

*

10

5

0

IL-4

IL-10

IFNγ

TGFβ

IL-12

IL-18

CTLA-4

GPR-83

SMAD-7

mRNA

slide22

Microscopic lesionmRNA expression

L6 (R)

*

25

L7 (S)

*

20

*

40 – mean Ct value

*

15

*

10

5

0

IL-4

IL-12

IL-18

TGFβ

GPR-83

SMAD-7

CTLA-4

mRNA

slide23

NAIVE CD4+ T CELL

APC

Th-2

T reg

Th-1

CYTOKINES AND T HELPER CELL DIFFERENTIATION

slide24

NAIVE CD4+ T CELL

Macrophage

APC

Th-2

T reg

Th-1

NK Cell

CTL

L6 Whole

Smad 7

L7 Whole

L7 Micro

IL 12

IL 4

Th-1, Th-2, T-reg ?

Inflammatory?

TGFβ

IL 4

IL10

IFN γ

IL 12

IL 18

slide26

Gene product

Th1

Th2

Treg

Inflammation

IL-2

1

ND

1

-1

IL-4

-1

1

1

ND

IL-6

1

-1

1

IL-8

ND

ND

1

1

IL-10

-1

1

1

0

IL-12

1

-1

ND

ND

IL-13

-1

1

ND

ND

IL-18

1

1

1

1

IFN-g

1

-1

1

1

TGF-b

-1

0

1

-1

CTLA-4

-1

-1

1

-1

GPR-83

-1

-1

1

-1

SMAD-7

1

1

-1

1

ND = No data

Step III. Inclusion of quantitative data to the phenotype scoring table and calculation of net affect.

Step I. GO-based Phenotype Scoring.

Step II. Multiply by quantitative data for each gene product.

slide27

Whole Tissue

L6 (R)

L7 (S)

120

100

80

Net Effect

60

40

20

0

Th-1

Th-2

T-reg

Inflammation

-

20

-40

slide28

Microscopic lesions

L6 (R)

60

L7 (S)

50

5mm

40

Net Effect

30

20

10

0

Th-1

Th-2

T-reg

-

10

Inflammation

Phenotype

-

20

slide29

Key points

  • Modeling is subordinate to the biological questions/hypotheses.
  • Togetherthe Gene Ontology and canonical genetic networks/pathways provide the central and complementary foundation for modeling functional genomics data.
  • The strategy you use to model your data will depend upon
    • what information is readily available for your species of interest
    • what biological system you are studying