course evaluation form
DESCRIPTION
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 PresentationTRANSCRIPT
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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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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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