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

2. 27803::Systems Biology2CBS, Department of Systems Biology Networks in electronics Lazebnik, Cancer Cell, 2002

3. 27803::Systems Biology3CBS, Department of Systems Biology Model Generation Interactions Lazebnik, Cancer Cell, 2002 Parts List YER001W YBR088C YOL007C YPL127C YNR009W YDR224C YDL003W YBL003C … YDR097C YBR089W YBR054W YMR215W YBR071W YBL002W YNL283C YGR152C … Sequencing Gene knock-out Microarrays etc. Interactions Genetic interactions Protein-Protein interactions Protein-DNA interactions Subcellular Localization Dynamics Microarrays Proteomics Metabolomics

4. 27803::Systems Biology4CBS, Department of Systems Biology Types of networks

5. 27803::Systems Biology5CBS, Department of Systems Biology Interaction networks in molecular biology Protein-protein interactions Protein-DNA interactions Genetic interactions Metabolic reactions Co-expression interactions Text mining interactions Association networks

6. 27803::Systems Biology6CBS, Department of Systems Biology Interaction networks in molecular biology Protein-protein interactions Protein-DNA interactions Genetic interactions Metabolic reactions Co-expression interactions Text mining interactions Association networks

7. 27803::Systems Biology7CBS, Department of Systems Biology Characterization of physical interactions Obligation –obligate (only found/function together) –non-obligate (can exist/function alone) Time of interaction –permanent (complexes, often obligate) –strong transient (require trigger, e.g. G proteins) –weak transient (dynamic equilibrium)

8. 27803::Systems Biology8CBS, Department of Systems Biology ol Examples: GPCR obligate, permanent non-obligate, strong transient

9. 27803::Systems Biology9CBS, Department of Systems Biology 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. 27803::Systems Biology10CBS, Department of Systems Biology 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. 27803::Systems Biology11CBS, Department of Systems Biology Issues with Y2H Strengths –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)

12. 27803::Systems Biology12CBS, Department of Systems Biology Protein interactions by immunoprecipitation 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.

13. 27803::Systems Biology13CBS, Department of Systems Biology Affinity Purification

14. 27803::Systems Biology14CBS, Department of Systems Biology 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)

15. 27803::Systems Biology15CBS, Department of Systems Biology Protein-protein interaction data growth Error rate may be as high as 30-50 %

16. 27803::Systems Biology16CBS, Department of Systems Biology Topology based scoring of interactions Low confidence (4 unshared interaction partners) High confidence (1 unshared interaction partners) ABC Yeast two- hybrid Low confidence (rarely purified together) High confidence (often purified together) Complex pull-downs D de Lichtenberg et al., Science, 2005

17. 27803::Systems Biology17CBS, Department of Systems Biology Filtering by subcellular localization de Lichtenberg et al., Science, 2005

18. 27803::Systems Biology18CBS, Department of Systems Biology 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

19. 27803::Systems Biology19CBS, Department of Systems Biology Filtering reduces coverage and increases specificity

20. Network Properties Graphs, paths, topology

21. 27803::Systems Biology21CBS, Department of Systems Biology 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

22. 27803::Systems Biology22CBS, Department of Systems Biology Sparse vs Dense G(V, E) –Where |V|=n the number of vertices –And |E|=m the number of edges Graph is sparse if m ~ n Graph is dense if m ~ n 2 Complete graph when m = (n 2 -n)/2 ~ n 2

23. 27803::Systems Biology23CBS, Department of Systems Biology Connected Components G(V,E) |V| = 69 |E| = 71

24. 27803::Systems Biology24CBS, Department of Systems Biology Connected Components G(V,E) |V| = 69 |E| = 71 6 connected components

25. 27803::Systems Biology25CBS, Department of Systems Biology Paths A path is a sequence {x 1, x 2,…, x n } such that (x 1,x 2 ), (x 2,x 3 ), …, (x n-1,x n ) are edges of the graph. A closed path x n =x 1 on a graph is called a graph cycle or circuit.

26. 27803::Systems Biology26CBS, Department of Systems Biology Shortest-Path between nodes

27. 27803::Systems Biology27CBS, Department of Systems Biology Shortest-Path between nodes

28. 27803::Systems Biology28CBS, Department of Systems Biology Longest Shortest-Path

29. 27803::Systems Biology29CBS, Department of Systems Biology Degree or connectivity Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004 Feb;5(2):101-13

30. 27803::Systems Biology30CBS, Department of Systems Biology 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  Such networks are said to be scale-free Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004 Feb;5(2):101-13

31. 17/04/2008Presentation name31CBS, Department of Systems biology 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 Example application: Network of interactions based on sexual partners

32. 27803::Systems Biology32CBS, Department of Systems Biology Knock-out lethality and connectivity

33. 27803::Systems Biology33CBS, Department of Systems Biology Clustering coefficient k: neighbors of I n I : edges between node I’s neighbors The density of the network surrounding node I, characterized as the number of triangles through I. Related to network modularity The center node has 8 (grey) neighbors There are 4 edges between the neighbors C = 2*4 /(8*(8-1)) = 8/56 = 1/7

34. 27803::Systems Biology34CBS, Department of Systems Biology 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 Protein complexes have a high clustering coefficient

35. 27803::Systems Biology35CBS, Department of Systems Biology Hierarchical Networks Barabási AL, Oltvai ZN. Nat Rev Genet. 2004

36. 27803::Systems Biology36CBS, Department of Systems Biology Detecting hierarchical organization Barabási AL, Oltvai ZN. Nat Rev Genet. 2004

37. 27803::Systems Biology37CBS, Department of Systems Biology 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.

38. 27803::Systems Biology38CBS, Department of Systems Biology Other interesting features Cellular networks are assortative, i.e. hubs tend not to interact directly with other hubs. Hubs have been claimed 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)