1. Biological Networks

2. Can a biologist fix a radio? Lazebnik, Cancer Cell, 2002

3. Building models from parts lists Lazebnik, Cancer Cell, 2002

4. Computational tools are needed to distill pathways of interest from large molecular interaction databases

5. Jeong et al. Nature 411, 41 - 42 (2001)

6. Nodes Links Interaction A B Network Proteins Physical Interaction Protein-Protein A B Protein Interaction Metabolites Enzymatic conversion Protein-Metabolite A B Metabolic Transcription factor Target genes Transcriptional Interaction Protein-DNA A B Transcriptional Different types of Biological Networks

7. gene A gene B regulates protein AProtein B binds Metabolite A Metabolite B Enzymatic reaction regulatory interactions (protein-DNA) functional complex (protein-protein) metabolic pathways Network Representation nodeedge

8. Network Analysis nodeedge Path Clique Hub

9. Scale Free vs Random Networks

10. Small-world Network Every node can be reached from every other by a small number of steps Social networks, the Internet, and biological networks all exhibit small-world network characteristics

12. What can we learn from a network?

13. Searching for critical positions in a network ?

14. High degree

15. Searching for critical positions in a network ? High closeness High degree

16. Searching for critical positions in a network ? High closeness High degree High betweenness

17. Features of cellular Networks hubs tend not to interact directly with other hubs. Hubs tend to be “older” proteins Hubs are evolutionary conserved Hubs are highly connected nodes

18. In a scale free network more proteins are connected to the hubs Albert et al. Science (2000) 406 378-382

19. In yeast, only ~20% of proteins are lethal when deleted Lethal Slow-growth Non-lethal Unknown Jeong et al. Nature 411, 41 - 42 (2001)

20. Networks can help to predict function

21. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002 Systematic phenotyping of 1615 gene knockout strains in yeast Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents) Screening against a network of 12,232 protein interactions

22. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002

23. Mapping the phenotypic data to the network Begley TJ, Mol Cancer Res. 2002

24. Networks can help to predict function Begley TJ, Mol Cancer Res. 2002.

25. Finding Local properties of Biological Networks: Network Motifs Network motifs are recurrent circuit elements. We can study a network by looking at its parts (or motifs) How many motifs are in the network? Adapted from :“An introduction to systems biology” by Uri Alon

26. Finding Local properties of Biological Networks: Motifs

30. What are these motifs? What biological relevance they have? Finding Local properties of Biological Networks: Motifs

31. Autoregulatory loop The probability of having autoregulatory loops in a random network is ~ 0 !!!!. Transcription networks: The regulation of a gene by its own product. Protein-Protein interaction network: dimerization

32. Autoregulatory loop Positive autoregulation Fast time-rise of protein level Negative autoregulation Stable steady state time [protein] time [protein] What is the effect of Autoregulatory loops on gene expression levels?

33. Three-node loops There are 13 possible structures with 3 nodes Feed forward loop XY Z Feedback loop XY Z But in biological networks you can find only 2!

34. Feedback loop XY Z

35. Course Summary

36. What did we learn Pairwise alignment – Local and Global Alignments When? How ? Tools : for local blast2seq, for global best use MSA tools such as Clustal X, Muscle

37. What did we learn Multiple alignments (MSA) When? How ? MSA are needed as an input for many different purposes: searching motifs, phylogenetic analysis, protein and RNA structure predictions, conservation of specific nts/residues Tools : Clustal X (for DNA and RNA), MUSCLE (for proteins) Tools for phylogenetic trees: PHYLIP …

38. What did we learn Search a sequence against a database When? How ? - BLAST :Remember different option for BLAST!!! (blastP blastN…. ), make sure to search the right database!!! DO NOT FORGET –You can change the scoring matrices, gap penalty etc - PSIBLAST Searching for remote homologies - PHIBLAST Searching for a short pattern within a protein

39. What did we learn Motif search When? How ? - Searching for known motifs in a given promoter (JASPAR) -Searching for overabundance of unknown regulatory motifs in a set of sequences ; e.g promoters of genes which have similar expression pattern (MEME) Tools : MEME, logo, Databases of motifs : JASPAR (Transcription Factors binding sites) PRATT in PROSITE (searching for motifs in protein sequences)

40. What did we learn Protein Function Prediction When? How ? - Pfam (database to search for protein motifs/domain (PfamA/PfamB) - PROSITE - Protein annotations in UNIPROT (SwissProt/ Tremble)

41. What did we learn Protein Secondary Structure Prediction- When? How ? –Helix/Beta/Coil(PHDsec,PSIPRED). –Predicts transmembrane helices (PHDhtm,TMHMM). –Solvent accessibility: important for the prediction of ligand binding sites (PHDacc).

42. What did we learn Protein Tertiary Structure Prediction- When? How ? – First we must look at sequence identity to a sequence with a known structure!! – Homology modeling/Threading – MODEBase- database of models Remember : Low quality models can be miss leading !! Tools : SWISS-MODEL,genTHREADER, MODEBase

43. What did we learn RNA Structure and Function Prediction- When? How ? – RNAfold – good for local interactions, several predictions of low energy structures – Alifold – adding information from MSA – RFAM – Specific database and search tools: tRNA, microRNA …..

44. What did we learn Gene expression When? How ? – Many database of gene expression GEO … – Clustering analysis EPClust (different clustering methods K-means, Hierarchical Clustering, trasformations row/columns/both…) –GO annotation (analysis of gene clusters..)

45. So How do we start … Given a hypothetical sequence predict it function…. What should we do???

46. Example Amyloids are proteins which tend to aggregate in solution. Abnormal accumulation of amyloid in organs is assumed to play a role in various neurodegenerative diseases. Question : can we predict whether a protein X is an amyolid ?