1. Bayesian network models of Biological signaling pathways karensachs@stanford.edu

2. K. Sachs 2 From Phospho-molecular profiling to Signaling pathways High throughput data Raf Erk p38 PKA PKC Jnk PIP2 PIP3 Plc  Akt... Cell1 Cell2 Cell3 Cell4 Cell600 Signaling Pathways Flow Measurments Picture: John Albeck

3. K. Sachs Outline  What are signaling pathways?  What kind of data is available study them?  How do we use Bayesian networks to learn their structure?  Two extensions:  Markov neighborhood algorithm  Bayesian network based cyclic networks (BBCs) 3

4. K. Sachs Outline  What are signaling pathways?  What kind of data is available study them?  How do we use Bayesian networks to learn their structure?  Two extensions:  Markov neighborhood algorithm  Bayesian network based cyclic networks (BBCs) 4

5. K. Sachs 5 Cell death ProliferationSecrete cytokines Cells respond to their environment Inside each cell is a molecular network

6. K. Sachs 6 “Central Dogma” Translation Protein DNA Transcription mRNA Modification Modified Protein ‘Blueprint’- instructions for production of all proteins Delivers instruction s for specific gene Ribosome: Protein- production factory

7. K. Sachs 7 Signaling & Genetic pathways A B C A B TF DNA RNA C Cell response

8. K. Sachs Outline  What are signaling pathways?  What kind of data is available study them?  How do we use Bayesian networks to learn their structure?  Two extensions:  Markov neighborhood algorithm  Bayesian network based cyclic networks (BBCs) 8

9. K. Sachs 9 Spectrum of Modeling Tools in Systems Biology

10. K. Sachs 10 Graph  Node: Measured level/activity of protein  Edge: Influence (dependency) between proteins Conditional probability distributions  Each node has a conditional probability given its parents Protein A Protein B Protein C Protein D Protein E Bayesian Networks P(B|A=‘On’) 0 1 2

11. K. Sachs How do we use Bayesian Networks to infer pathways? 11 The Technical Details  Score candidate models  Use a heuristic search to find high scoring models (analytical solution!)

12. K. Sachs 12 Protein data  Western blot

13. K. Sachs 13 Protein data  Protein arrays

14. K. Sachs 14 Protein data  Mass Spectrometry All of these lysate approaches give 1 measurement per protein for 10^3-10^7 cells

15. K. Sachs 15 Flow Cytometry: Single Cell Analysis Thousands of datapoints

16. K. Sachs 16 MEK3/6 MAPKKK PLC  Erk1/2 Mek1/2 Raf PKC p38 Akt MAPKKK MEK4/7 JNK LATLAT Lck VAV SLP-76 RAS PKA 123 CD28 CD3 PI3K LFA-1 Cytohesin Zap70 PIP3 PIP2 JAB-1 Activators 1.  -CD3 2.  -CD28 3. ICAM-2 4. PMA 5.  2cAMP Inhibitors 6. G06976 7. AKT inh 8. Psitect 9. U0126 10. LY294002 10 5 4 6 7 9 8 Stimulations and perturbations

17. K. Sachs 17 Datasets of cells condition ‘a’ condition ‘b’ condition…‘n’ Raf Mek1/2 Erk p38 PKA PKC Jnk PIP2PIP3 Plc  Akt 12 Color Flow Cytometry perturbation a perturbation n perturbation b Conditions (multi-well format) T-Lymphocyte Data  Primary human T-Cells  9 conditions  (6 Specific interventions)  9 phosphoproteins, 2 phospolipids  600 cells per condition  5400 data-points Omar Perez

18. K. Sachs 18 Statistical Dependencies A B C D E Phospho A Phospho B

19. K. Sachs 19 Statistical Dependencies Edges can be directed (primarily) due to the use of interventions A B C D E Phospho A Phospho B

20. K. Sachs 20 Overview Influence diagram of measured variables Bayesian Network Analysis Datasets of cells condition ‘a’ condition ‘b’ condition…‘n’ Raf Mek1/2 Erk p38 PKA PKC Jnk PIP2PIP3 Plc  Akt Multiparameter Flow Cytometry perturbation a perturbation n perturbation b Conditions (multi well format)

21. K. Sachs 21 PKC Raf P44/42 Mek Plc  PKA Akt Jnk P38 PIP2 PIP3 Phospho-Proteins Phospho-Lipids Perturbed in data Inferred Network

22. K. Sachs 22 PKC Raf P44/42 Mek Plc  PKA Akt Jnk P38 PIP2 PIP3 Phospho-Proteins Phospho-Lipids Perturbed in data How well did we do? Direct phosphorylation

23. K. Sachs 23 Features of Approach  Direct phosphorylation: Mek Difficult to detect using other forms of high-throughput data: -Protein-protein interaction data -Microarrays Erk

24. K. Sachs 24 PKC Raf P44/42 Mek Plc  PKA Akt Jnk P38 PIP2 PIP3 Phospho-Proteins Phospho-Lipids Perturbed in data How well did we do?

25. K. Sachs 25 PKC Raf P44/42 Mek Plc  PKA Akt Jnk P38 PIP2 PIP3 Phospho-Proteins Phospho-Lipids Perturbed in data How well did we do? Indirect Signaling

26. K. Sachs 26  Indirect signaling  Dismissing edges RafMek Erk PKCJnk PKC Mapkkk Jnk Not measured Mek4/7 Indirect connections can be found even when the intermediate molecule(s) are not measured Indirect signaling

27. K. Sachs 27 Indirect signaling - Complex example  Is this a mistake?  The real picture  Phoso-protein specific  More than one pathway of influence PKCRaf Mek PKC Raf s259 Mek Raf s497 Ras

28. K. Sachs 28 PKC Raf P44/42 Mek Plc  PKA Akt Jnk P38 PIP2 PIP3 Expected Pathway  15/17 Classic Phospho-Proteins Phospho-Lipids Perturbed in data How well did we do?

29. K. Sachs 29 PKC Raf Erk Mek Plc  PKA Akt Jnk P38 PIP2 PIP3 Expected Pathway Reported Missed  15/17 Classic  17/17 Reported  3 Missed Reversed Phospho-Proteins Phospho-Lipids Perturbed in data Signaling pathway reconstruction [Sachs et al 2005]

30. K. Sachs Caveats  Inhibitor specificity  Binding site similar across proteins  Reagent availability and specificity  Data quality  These are issues in many biological apps! 30 I think I’ll bind here

31. K. Sachs Outline  What are signaling pathways?  What kind of data is available study them?  How do we use Bayesian networks to learn their structure?  Two extensions:  Markov neighborhood algorithm  Bayesian network based cyclic networks (BBCs) 31

32. K. Sachs 32 Markov Neighborhood Algorithm

33. K. Sachs 33 Building larger networks  12 color capability  Model 50-100 variables  4 color capability  Model 12 variables PKC Raf P44/42 Mek Plc  PKA Akt Jnk P38 PIP2 PIP3 ~80 proteins involved in MAPK signaling (11- at the cutting edge- is NOT enough!)

34. K. Sachs 34 Measured subsets = Incomplete dataset (Missing data)  Insufficient information for standard approaches (will perform poorly)  Use a set of biologically motivated assumptions to constrain search..  And to reduce the number of experiments ( ) 11 4 = 330

35. K. Sachs 35 Constraining the search  Plus potential perturbation parents Identify candidate parents  Using ‘Markov neighborhoods’ (for each variable)

36. K. Sachs 36 Bayesian Network Analysis (Constrained search) Raf Mek1/2 Erk p38 PKA PKC Jnk PIP2 PIP3 Plc  Akt Molecules 1, 3, 7, 9 Molecules 2, 4, 7, 10 Molecules 1, 2, 6, 11 Approach overview

37. K. Sachs 37 Neighborhood reduction C B E D A F 4 color capability Conditional independencies in the substructure? ABCABC 4  11

38. K. Sachs 38 Accurate Reproduction of Model ~15 experiments, 4-colors Confidence value different from original model PKC Raf Erk Mek Plc  Akt Jnk P38 PIP2 PIP3 PKA

39. K. Sachs 39 Raf Mek1/2 Erk p38 PKA PKC Jnk PIP2 PIP3 Plc  Akt Active learning approach

40. K. Sachs Outline  What are signaling pathways?  What kind of data is available study them?  How do we use Bayesian networks to learn their structure?  Two extensions:  Markov neighborhood algorithm  Bayesian network based cyclic networks (BBCs) 40

41. K. Sachs 41 Learning cyclic structures with Bayesian networks  Biological networks contain many loops  Bayesian networks are constrained to be acyclic So…

42. K. Sachs Overcoming acyclicity  Signaling pathways contain many cycles  Bayesian networks are constrained to be acyclic  How can we accurately model pathways with cycles? 42 GRB2/SOS Raf MEK Erk Ras Develop a new, Bayesian network derived algorithm that models cycles… 

43. K. Sachs Bayesian Network Based Cyclic Networks (BBNs)  I. Break loops with molecule inhibitors  II. Use BN to learn the structure (now not cyclic!)  III. Close loops 43 GRB2/SOS Raf MEK Erk Ras Mek inhibitor Solomon Itani

44. K. Sachs 44 GRB2/SOS Raf MEK Erk Ras  I. Break loops with molecule inhibitors  Detect loops P(A) A* ~= P(A)  II. Use BN to learn the structure (now not cyclic!)  III. Close loops P(B|Pa(B)) A* ~= P(B|Pa(B)) ABAB Bayesian Network Based Cyclic Networks (BBNs)

45. K. Sachs 45 Future work  Larger network from overlapping sets (Markov neighborhood)  Dynamic models over time  Differences in signaling (sub-populations, treatment conditions, cell types, disease states)

46. K. Sachs 46 Acknowledgements 46 Shigeru Okumura Funding LLS post doctoral fellowship Solomon Itani Garry Nolan Dana Pe’er Doug Lauffenburger Omar Perez Dennis Mitchell Mesrob Ohannessian

47. Extra slides

48. Mathematical Intuition B B C C C is independent of A given B. A A A A B B C C D D C independent of A given B and D 1)No need to introduce time!!! 2) When loops are broken, the result is a BN!!!

49. K. Sachs 49 Prediction: Erk  Akt Erk1/2 unperturbed  Erk  Akt not well established in literature Predictions:  Erk1/2 influences Akt  While correlated, Erk1/2 does not influence PKA PKC Raf Erk1/2 Mek PKA Akt

50. K. Sachs 50 Validation control, stimulated Erk1 siRNA, stimulated  SiRNA on Erk1/Erk2  Select transfected cells  Measure Akt and PKA P-AktP-PKA P=9.4e -5 P=0.28