1. Biological networks: Types and origin
Protein-protein interactions, complexes, and network properties

2. Networks in electronics
Radio kindly provided by Lazebnik, Cancer Cell, 2002

3. Interactions Model Generation Parts List Interactions Dynamics
Sequencing
Gene knock-out
Microarrays
etc.
Model
Generation
YER001W
YBR088C
YOL007C
YPL127C
YNR009W
YDR224C
YDL003W
YBL003C …
YDR097C
YBR089W
YBR054W
YMR215W
YBR071W
YBL002W
YNL283C
YGR152C …
Parts List
Genetic interactions
Protein-Protein interactions
Protein-DNA interactions
Subcellular Localization
Interactions
Microarrays
Proteomics
Metabolomics
Dynamics
Radio kindly provided by Lazebnik, Cancer Cell, 2002

4. Types of networks

5. Interaction networks in molecular biology
Protein-protein interactions
Protein-DNA interactions
Genetic interactions
Metabolic reactions
Co-expression interactions
Text mining interactions
Association networks

6. Interaction networks in molecular biology
Protein-protein interactions
Protein-DNA interactions
Genetic interactions
Metabolic reactions
Co-expression interactions
Text mining interactions
Association networks

7. Characterization of physical interactions
Obligation
obligate (protomers only found/function together)
non-obligate (protomers can exist/function alone)
Time of interaction
permanent (complexes, often obligate)
strong transient (require trigger, e.g. G proteins)
weak transient (dynamic equilibrium)

8. Examples: GPCR ol
obligate, permanent
non-obligate,
strong transient

9. Approaches by interaction type
Physical Interactions
Yeast two hybrid screens
Affinity purification (mass spec)
Protein-DNA by chIP-chip
Other measures of ‘association’
Genetic interactions (double deletion mutants)
Functional associations (STRING)
Co-expression

10. Yeast two-hybrid method
Y2H assays interactions in vivo.
Uses property that transcription factors generally have separable transcriptional activation (AD) and DNA binding (DBD) domains.
A functional transcription factor can be created if a separately expressed AD can be made to interact with a DBD.
A protein ‘bait’ B is fused to a DBD and screened against a library of protein “preys”, each fused to a AD.

11. Yeast two-hybrid method
Fields and Song

12. Issues with Y2H Strengths Weaknesses: False positive interactions
High sensitivity (transient & permanent PPIs)
Takes place in vivo
Independent of endogenous expression
Weaknesses: False positive interactions
Auto-activation
‘sticky’ prey
Detects “possible interactions” that may not take place under real physiological conditions
May identify indirect interactions (A-C-B)
Weaknesses: False negatives interactions
Similar studies often reveal very different sets of interacting proteins (i.e. False negatives)
May miss PPIs that require other factors to be present (e.g. ligands, proteins, PTMs)

13. Protein interactions by immuno-precipitation followed by mass spectrometry
Start with affinity purification of a single epitope-tagged protein
This enriched sample typically has a low enough complexity to be fractionated on a standard polyacrylamide gel
Individual bands can be excised from the gel and identified with mass spectrometry.

14. Affinity Purification

15. Affinity Purification
Strengths
High specificity
Well suited for detecting permanent or strong transient interactions (complexes)
Detects real, physiologically relevant PPIs
Weaknesses
Less suited for detecting weaker transient interactions (low sensitivity)
May miss complexes not present under the given experimental conditions (low sensitivity)
May identify indirect interactions (A-C-B)

16. Protein-protein interaction data growth
Error rate may be as high as %

17. Topology based scoring of interactions
Yeast two-hybrid A B C
High confidence (1 unshared interaction partners)
Low confidence (4 unshared interaction partners)
Low confidence (rarely purified together)
High confidence (often purified together)
Complex pull-downs
de Lichtenberg et al., Science, 2005

18. Filtering by subcellular localization
de Lichtenberg et al., Science, 2005

19. Reducing the error rate in PPI data
Benchmark by measuring the overlap between the curated MIPS complexes and different PPI data sets derived from high-throughput screens.
Overlap with curated MIPS complexes
Estimated error rate
de Lichtenberg et al., Science, 2005

20. Filtering reduces coverage and increases specificity

21. Network Properties
Graphs, paths, topology

22. Graphs Graph G=(V,E) is a set of vertices V and edges E
A subgraph G’ of G is induced by some V’  V and E’  E
Graph properties:
Connectivity (node degree, paths)
Cyclic vs. acyclic
Directed vs. undirected

23. Sparse vs Dense
G(V, E) where |V|=n, |E|=m the number of vertices and edges
Graph is sparse if m~n
Graph is dense if m~n2
Complete graph when m=n2

24. Connected Components
G(V,E)
|V| = 69
|E| = 71

25. Connected Components
G(V,E)
|V| = 69
|E| = 71
6 connected components

26. Paths
A path is a sequence {x1, x2,…, xn} such that (x1,x2), (x2,x3), …, (xn-1,xn) are edges of the graph.
A closed path xn=x1 on a graph is called a graph cycle or circuit.

27. Shortest-Path between nodes

28. Shortest-Path between nodes

29. Longest Shortest-Path

30. Degree or connectivity

31. Random vs scale-free networks
P(k) is probability of each degree k, i.e fraction of nodes having that degree.
For random networks, P(k) is normally distributed.
For real networks the distribution is often a power-law:
P(k) ~ k-g
Such networks are said to be scale-free

32. Lewin Bo, et al., Sex i Sverige; Om sexuallivet i Sverige 1996,
“The Swedish sex web”
Target the ‘hubs’ to have an efficient safe sex education campaign
Lewin Bo, et al., Sex i Sverige; Om sexuallivet i Sverige 1996,
Folkhälsoinstitutet, 1998

33. Knock-out lethality and connectivity

34. Clustering coefficient
The density of the network surrounding node I, characterized as the number of triangles through I. Related to network modularity
k: neighbors of I
nI: edges between node I’s neighbors
The center node has 8 (grey) neighbors
There are 4 edges between the neighbors
C = 2*4 /(8*(8-1)) = 8/56 = 1/7

35. Protein complexes have a high clustering coefficient
Proteins subunits are highly interconnected and thus have a high clustering coefficient
There exists algorithms, such as MCODE, for identifying subnetworks (complexes) in large protein-protein interaction networks

36. Hierarchical Networks

37. Detecting hierarchical organization

38. Scale-free networks are robust
Complex systems (cell, internet, social networks), are resilient to component failure
Network topology plays an important role in this robustness
Even if ~80% of nodes fail, the remaining ~20% still maintain network connectivity
Attack vulnerability if hubs are selectively targeted
In yeast, only ~20% of proteins are lethal when deleted, and are 5 times more likely to have degree k>15 than k<5.

39. Other interesting features
Cellular networks are assortative, hubs tend not to interact directly with other hubs.
Hubs tend to be “older” proteins (so far claimed for protein-protein interaction networks only)
Hubs also seem to have more evolutionary pressure—their protein sequences are more conserved than average between species (shown in yeast vs. worm)