1. D ISCOVERING REGULATORY AND SIGNALLING CIRCUITS IN MOLECULAR INTERACTION NETWORK Ideker Bioinformatics 2002 Presented by: Omrit Zemach April 3 2013 Seminar in Algorithmic Challenges in Analyzing Big Data* in Biology and Medicine-TAU

2. OUTLINE Introduction- biological terms Motivation Methods Basic z-score calculation simulated annealing Results Discussion

3. P ROTEIN -P ROTEIN INTERACTION All living organisms consist of living cells All those cells, comprise the same building blocks: RNA,DNA and PROTEIN Protein sequences are encoded in DNA Proteins play major roles in all cellular processes

4. DNA REPLICATION TRANSCIPTION INTO mRNA TRANSLATION OF mRNA

5. P ROTEIN -DNA INTERACTIONS protein binds a molecule of DNA Regulate the biological function of DNA,biological function usually the expression of a gene.expressiongene Transcription factors that activate or repress gene expressionTranscription factors

6. G ENE E XPRESSION Gene is a sequence of the DNA. The gene decodes to a protein. the process by which information from a gene is used in the synthesis of a functional protein is called gene expression It is interesting to test gene expression on multiple conditions (experiments). Differential- express

7. DNA chips/ Microarrays -Simultaneous measurement of expression levels of all genes.

8. M OTIVATION Databases of PROTEIN-PROTEIN & PROTEIN-DNA interactions Widely available mRNA expression data Generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression

9. M OTIVATION Exposing the yeast galactose utilization pathway to 20 perturbations Constructing a molecular interaction network by screening a database of protein- protein and protein-DNA interactions Select 362 interactions linking genes that were differentially-expressed under one or more perturbations. Analyze changes in expression.

11. Conclusion: Pairs of genes linked in this network were more likely to have correlated expression profiles than genes chosen at random however, the general task of Associating gene expression changes with higher order groups of interaction was not discussed

12. D ISCOVERING REGULATORY AND SIGNALING CIRCUITS IN MOLECULAR INTERACTION NETWORKS Introducing method for searching the networks to find ‘active sub-networks’ On multiple conditions, determine which conditions significantly affect gene expression in each subnetwork.

13. M ETHODS

14. Z- SCORE CALCULATION Given each gene i a value p i p i= The significance of differential expression of gene I z i = Ф -1 (1- p i ) ( z-score for gene i) aggregate z-score for subnetwork A Calibrating z against the background distribution

16. S CORING OVER MULTIPLE CONDITIONS Extending the scoring system over multiple conditions. Create a matrix of z-score. Rows- m conditions Columns-genes Produce m different aggregate scores (one for each condition Sort them from highest to lowest. compute r A max = max j (r A[j] )

17. Compute r A[j] for each j=1….m as follows: P Z = 1 – Ф( Z A[j] ) (the probability that any single condition has a z- score above Z A[j] ) b (the probability that at least j of the m conditions had scores above Z A[j] ) r A[j] = Ф - 1 (1-p A[j) ) r A max = max j (r A[j] ) compute r A max

18. Z score of gene 1 Conditio n 1 Conditio n 2 Conditio n 3 Conditio n 4

19. Aggregate scores of z A1 ….. z Amc Aggregate scores of z A1 ….. z Am sorted Computing r A[1] … r A[m] Taking max j (r A[j] ) Calibrating z against the background distribution

20. S IMULATED ANNEALING strategy to find local maximum we must sometimes select new points that do not improve solution Annealing - Gradual cooling of liquid Incorporate a temperature parameter into the maximization procedure At high temperatures, explore parameter space At lower temperatures, restrict exploration

21. S IMULATED A NNEALING S TRATEGY Start with some sample Propose a change Decide whether to accept change

22. S IMULATED A NNEALING S TRATEGY Decide whether to accept change- HOW?? Consider decreasing series of temperatures For each temperature, iterate these steps: Propose an update and evaluate function Accept updates that improve solution Accept some updates that don't improve solution Acceptance probability depends on “temperature” parameter

23. S EARCHING FOR HIGH SCORING SUBNETWORKS VIA SIMULATED ANNEALING Associate an active/inactive state with each node G W = denote the working sub graph of G induced by the active nodes

24. T HE ALGORITHM

25. H EURISTICS FOR IMPROVED ANNEALING Search for M subnetworks simultaneously Increasing the efficiency of annealing in networks with many ‘hubs’

26. High score node

27. Solution- changing step 3

28. Defining d min at the beginning of the algorithm If deg(node)> dmin Remove all neighbors that are not in the top scoring component Solution- changing step 3

29. R ESULTS

30. Small network with a single perturbation 7.7 3.1 2.3 2.8 2.5 Z-scores

31. GAL4

32. TRANSCRIPTION FACTOR

33. Simulated annealing was preformed with parameters: N=100,000 Tstart= 1 Tend= 0.01 M=5 dmin=100

34. Distribution of sub-network score in actual and randomized data

35. Large network with several perturbation

38. D ISCUSSION

39. S UBNETWORKS ARE CONSISTS WITH KNOWN REGULATORY CIRCUITS

40. S UBNETWORKS VERSUS GENE EXPRESSION CLUSTERS Our approach groups genes subject to the constraints of molecular interaction network Subnetworks are scored over only a subset of conditions Groups genes only by the significance of change, while clustering methods groups genes by both magnitude and direction of change Our method leaves some genes unaffiliated with any subnetwork, unlike clustering which assign every gene to distinct cluster

41. F UTURE WORK Investigating the subnetworks we found in the laboratory Accommodating new types of interaction networks (protein and small molecules) Annotating each interaction with its directionally compartments