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Course Evaluation Form About The Course -Go more slowly (||) -More lectures (||) -Problem Sets, Class Projects (|||) -Software tools About The Instructor -Accessible out of class (Office Hours, Thursdays 2pm-5pm) -Course Discussion

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Course Evaluation Form. About The Course -Go more slowly (||) More lectures (||) Problem Sets, Class Projects (|||) -Software tools. About The Instructor -Accessible out of class (Office Hours, Thursdays 2pm-5pm) Course Discussion. Networks in Biology. Today’s Lecture - PowerPoint PPT Presentation

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Page 1: Course Evaluation Form

Course Evaluation Form

About The Course-Go more slowly (||)-More lectures (||)-Problem Sets, Class Projects (|||) -Software tools

About The Instructor-Accessible out of class (Office Hours, Thursdays 2pm-5pm)-Course Discussion

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Networks in Biology

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Today’s Lecture

- The Cell as a Complex System-Types of Molecular Networks-Some Results on the Structure of Molecular Networks(Hierarchical Structure, Date and Party Hubs)- Disease Networks- Impacts of Social Networks in Biology

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http://multimedia.mcb.harvard.edu/media.html

Inner Life of the Cell

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protein-gene interactions

protein-protein interactions

PROTEOME

GENOME

Citrate Cycle

METABOLISM

Bio-chemical reactions

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Citrate Cycle

METABOLISM

Bio-chemical reactions

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Metabolic NetworkNodes: chemicals (substrates)

Links: bio-chemical reactions

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Metabolic network

Organisms from all three domains of life are scale-free networks!

H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, 407 651 (2000)

Archaea Bacteria Eukaryotes

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Modular vs. Scale-free Topology

Scale-free

(a)

Modular

(b)

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Global network properties

A.-L. B. and Z.N. Oltvai, Nat. Rev. Gen.(2004)

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Hierarchical Networks3. Clustering coefficient scales

C(k)= # links between k neighborsk(k-1)/2

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Scaling of the clustering coefficient C(k)

The metabolism forms a hierachical network.

Ravasz, Somera, Mongru, Oltvai, A-L. B, Science 297, 1551 (2002).

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Characterizing the links

Metabolism:Flux Balance Analysis (Palsson)Metabolic flux for each reaction

Edwards, J. S. & Palsson, B. O, PNAS 97, 5528 (2000).Edwards, J. S., Ibarra, R. U. & Palsson, B. O. Nat Biotechnol 19, 125 (2001). Ibarra, R. U., Edwards, J. S. & Palsson, B. O. Nature 420, 186 (2002).

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Global flux organization in the E. coli metabolic network

E. Almaas, B. Kovács, T. Vicsek, Z. N. Oltvai, A.-L. B. Nature, 2004.

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protein-gene interactions

protein-protein interactions

PROTEOME

GENOME

Citrate Cycle

METABOLISM

Bio-chemical reactions

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protein-protein interactions

PROTEOME

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Topology of the protein network

)exp()(~)( 00

k

kkkkkP

H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001)

Nodes: proteins

Links: physical interactions (binding)

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Origin of the scale-free topology: Gene Duplication

Perfect copy Mistake: gene duplication

Wagner (2001); Vazquez et al. 2003; Sole et al. 2001; Rzhetsky & Gomez (2001); Qian et al. (2001); Bhan et al. (2002).

Proteins with more interactions are more likely to get a new link:Π(k)~k (preferential attachment).

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Yeast protein network- lethality and topological position -

Highly connected proteins are more essential (lethal)...

H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001)

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Protein Network Dynamics

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Disease Networks

Genotypic Association Between Diseases

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Gene network

GENOME

PHENOMEDISEASOME

Disease network

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Disease NetworkGoh et al. PNAS 2007

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Mental Diseases

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p53 network (mammals)

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Lethal Genes Disease Genes

Goh et al. PNAS 2007

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Schematic functional organization

Functional Core– Expressed in most

tissues (housekeeping)– High degree– High coexpression– Lethal

Functional Periphery

– Tissue-specific expression– Low degree– Low coexpression– Low lethality

Lack of disease genes

Enrichment of disease genes

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Phenotypic Association Between Diseases

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Disease 1 Disease 2Affect Same Individuals

Significantly more than Random

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~ 13’039’018 patients

~ 32’341’348 records (hospitalizations)

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P2

N

jiijij PPNCR /

Building a Net

C12

P1

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Word of Caution

PNPPNCR jiijij //

Perfectly correlated diseases, P2 = C12

~1

~N

Underestimates overlapof Common Phenotypes

Overestimate overlapof Rare Phenotypes

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Sibling having multiple sclerosis 1

Sibling having rheumatoid arthritis 1

1 Harley JB. Nature Genetics 39(9) 1053 (2007)2 Scott LJ et al, Science 316 1341 (2007)

First Degree Relatives Having Diabetes2

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C12

P1 P2

N

))(( jiji

jiijij

PNPNPP

PPNC

Building a Net

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Word of Caution

1)(

)(

))((

PNP

PNP

PNPNPP

PPNC

jiji

jiijij

Perfectly correlated diseases & P1 = P2 = C12

)(

)(

))(( 21

12

PNP

PNP

PNPNPP

PPNC

jiji

jiijij

Perfectly correlated diseases & P1 >> P2 = C12

If N >> P1, P2

1

2

P

Pij

Example P2 = 1/10,000 P1 =1/100

1.0ij

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-0.1 0 0.1 0.2 0.3 0.4 0.50

1

2

3

4

5

6

7

ij

log 1

0 Num

ber

of P

airs

66.8%-0.1 0 0.1 0.2 0.3 0.4 0.5

0

1

2

3

4

5

6

7

ij

log 1

0 Num

ber

of P

airs

66.8%

Positive Associations

Diseases appear are more likely to be correlated than anti-correlated

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Visualizing the Network

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http://hudine.neu.edu