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 Approaches by interaction/method type Physical Interactions –Yeast two hybrid screens (PPI) –Affinity purification mass spectrometry, APMS (PPI) –Protein complementation assays (PPI) –ChIP-Seq, ChIP-Chip (protein-DNA) –CLIP-Seq, RIP-Seq, HITS-CLIP, PAR-CLIP (protein-RNA) Other measures of ‘association’ –Genetic interactions (double deletion mutants) –Co-expression –Functional associations –STRING (which includes many of the above and more)

7. 27803::Systems Biology7CBS, 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.

8. 27803::Systems Biology8CBS, Department of Systems Biology Issues with Y2H Strengths –Takes place in vivo –Independent of endogenous expression Weaknesses: False positive interactions –Detects “possible interactions” that may not take place under 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)

9. 27803::Systems Biology9CBS, Department of Systems Biology Protein complementation assay (PCA)

10. 27803::Systems Biology10CBS, Department of Systems Biology Protein interactions by immunoprecipitation followed by mass spectrometry (APMS) Start with affinity purification of a single epitope-tagged protein This enriched sample typically has a low enough complexity to be fractionated by electrophoresis techniques

11. 27803::Systems Biology11CBS, Department of Systems Biology Affinity Purification Mass Spec Strengths –High specificity –Well suited for detecting permanent or strong transient interactions (complexes) –Detects real, physiologically relevant PPIs Weaknesses –Lower sensitivity: Less suited for detecting weaker transient interactions –May miss complexes not present under the given experimental conditions (low sensitivity) –May identify indirect interactions (A-C-B)

12. 27803::Systems Biology12CBS, Department of Systems Biology Recent binary PPI network Y2H by Yu et al. 2008 : 2018 proteins, 2930 interactions PCA by Tarassov et al. 2008 : 1124 proteins, 2770 interactions

13. 27803::Systems Biology13CBS, Department of Systems Biology Other characterizations 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) Location/compartmentalization constraints –Same/different cellular compartment –Tissue specificity

14. 27803::Systems Biology14CBS, Department of Systems Biology Growth of PPI data: IntAct Statistics

15. 27803::Systems Biology15CBS, Department of Systems Biology IntAct Statistics

16. 27803::Systems Biology16CBS, Department of Systems Biology IntAct Statistics

17. 27803::Systems Biology17CBS, Department of Systems Biology iRefIndex integration of PPI DBs http://irefindex.uio.no/wiki/iRefIndex

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

19. 27803::Systems Biology19CBS, Department of Systems Biology Filtering by subcellular localization de Lichtenberg et al., Science, 2005

20. 27803::Systems Biology20CBS, Department of Systems Biology An example binary-interaction score For the yeast two-hybrid experiments, the reliability of an interaction has been found to correlate well with the number of non-shared interaction partners for each interactor [6]. This can be summarized in the following raw quality score where N A and N B are the numbers of non-shared interaction partners for an interaction between protein A and B. Low confidence (4 unshared interaction partners) High confidence (1 unshared interaction partners) ABC D

21. 27803::Systems Biology21CBS, Department of Systems Biology An example “pull-down” interaction score For APMS or other IP pull-down experiments, the reliability of the inferred binary interactions has been found to correlate better with the number of times the proteins were co-purified vs. purified individually. where: –N AB is the number of purifications containing both proteins, i.e. the intersection of experiments that find them, –N AB is the total number of purifications that find either A or B, i.e. the union of experiments that find them, –N A is the number of purifications containing A, and –N B is the numbers of purifications containing B

22. 27803::Systems Biology22CBS, Department of Systems Biology Filtering reduces coverage and increases specificity

23. Network Properties Graphs, paths, topology

24. 27803::Systems Biology24CBS, 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

25. 27803::Systems Biology25CBS, 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

26. 27803::Systems Biology26CBS, Department of Systems Biology Connected Components G(V,E) |V| = 69 |E| = 71

27. 27803::Systems Biology27CBS, Department of Systems Biology Connected Components G(V,E) |V| = 69 |E| = 71 6 connected components

28. 27803::Systems Biology28CBS, 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.

29. 27803::Systems Biology29CBS, Department of Systems Biology Shortest-Path between nodes

30. 27803::Systems Biology30CBS, Department of Systems Biology Shortest-Path between nodes

31. 27803::Systems Biology31CBS, Department of Systems Biology Longest Shortest-Path

32. 27803::Systems Biology32CBS, 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

33. 27803::Systems Biology33CBS, 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

34. 27803::Systems Biology34CBS, Department of Systems Biology Essentiality vs node degree

35. 27803::Systems Biology35CBS, 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 neighbors (green) There are 4 edges between these neighbors C = 1/7

36. 27803::Systems Biology36CBS, 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

37. 27803::Systems Biology37CBS, Department of Systems Biology Hierarchical Networks Barabási AL, Oltvai ZN. Nat Rev Genet. 2004

38. 27803::Systems Biology38CBS, Department of Systems Biology Detecting hierarchical organization Barabási AL, Oltvai ZN. Nat Rev Genet. 2004

39. 27803::Systems Biology39CBS, 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

40. 27803::Systems Biology40CBS, 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)