Systems Biology II

1. Systems Biology II

2. Roadmap • Review from a long time ago when we last visited this topic. • Review of some work we have done using a systems biology approach. • Look at some research that benefited by adopting systems biology approaches.

3. “Inner life of a Cell”SIGGRAPH 2006 showcase winner • Need to fight infection • WBC • Need to keep blood from leaking out

4. Two ways of looking a problem • Top down or bottom up • Either look at the whole organism and abstract large portions of it • Or try to understand each small piece and then after understanding every small piece assemble into the whole • Both are used, valid and complement each other

5. Theoretical types of control

6. Expression measurements

7. Blue line (pp) Yellow line (pd) Visualizing the data

8. Graph theory, networks • Two types of networks • Exponential and scale free • Most cellular networks are scale free • It makes the most sense to study the interactions of the central nodes not the outer nodes

9. Using network properties of a large complex data-set to evaluate the correlation of gene expression from a large microarray experiment

10. … Design of initial experiment SHR-SP SR/JR/HSD ♂ ♀ F1 rats 120 ♂F2 rats mRNA of whole eyes Gene expression Genotyping

11. Trans Summary of eQTL linkages Cis Transcript Location Marker Location

12. NPCE: Non-Positional Correlation of Expression Capture bio-relatedness Pair-wise correlation Macromolecular structures Metabolic pathways Disease Genes Devoid of marker information More information not dependant on marker density more noise

13. Strongly correlated genes r2 = 0.78 Expression BBS7 (log2) Expression BBS3 (log2)

14. Weak correlation r2 = 0.16 Expression ABCA4 (log2) Expression BBS4 (log2)

15. Distribution of r2

16. Combining pathway information with correlation

17. Looking at known pathways a simple cutoff value is not identifiable Partial correlation or multiple correlations More feasible but, still difficult May only work in a subset of pathways Most useful if you want to confirm membership to a known group? Difference between random and known pathways is small Another way? Pairwise correlations are not enough?

18. Networks

19. “Realworld” Networks • Tend to be highly clustered • Tend to have short path lengths • Many nodes with few interactions • Few nodes with many interactions

20. Useful tools • Cytoscape • Best for visualization • Limited (for us anyway) number of nodes • http://www.cytoscape.org/ • Networkx • Python module • Visualization and network discription -https://networkx.lanl.gov/

21. Using network properties • Can we use networks to identify “critical” genes? • Is it possible to determine a usable “cutoff” for correlations used to make the network • What correlation value will give a usable, relevant network? • Is this value similar to the p value determined from the distribution of correlations? • Is it possible to use network properties to identify a grouping of interacting genes (ex. pathway, subunits or other interactions)

22. Highly Connected genes

23. Molecular function Most common - none glutamate-ammonia ligase activity GTPase activator activity carrier activity structural molecule activity DNA binding Biological process nitrogen fixation Transport vesicle fusion cell motility small GTPase mediated signal transduction Common ontologies

24. What correlation level to use

25. Other parameters

26. Validating a graph biological relevance • Need to use information to pick a correlation level(s) used to construct a graph. • After the graph is constructed • How well does it predict known bio-interactions

27. Validating against pathways • Kegg has a nice collection of pathway annotations (http://www.genome.jp/kegg/) • Also have a webservice interface • Allows programatic access to pathway annotations (http://www.genome.jp/kegg/soap/) • By species • By pathway • By pathway type • Some problems kegg id vs affy probe id • May be a many to many relationship

28. Rattus norvegicus (rat) metabolic pathways • Kegg has 110 metabolic pathways • Range in size from 3 members to 100’s of members • Examples: • Novobiocin biosynthesis • ATP synthesis • Fructose and mannose metabolism

29. Path length

30. Path coverage

31. Different values • Using a correlation of .9 • No coverage for either pathway or random set • Not enough connections, they may be significant, but only a small fraction are present • Lower correlations • Less clear • Much larger networks

32. Why networks != correlations

33. Abca4 Bbs11 Bbs2 Bbs6 Bbs1 Bbs7 Bbs8 Bbs5 Bbs9 p < 0.001 Bbs3 p < 0.0002 Bbs4 0.45-0.54 0.55-0.64 0.65+

35. Conclusions • Network properties show promise as a way to look at this data • Pair-wise correlations and networks are unable to predict pathways or other interactions with certainty • But they can help • Using network tools and frameworks is a way to manage and simplify analysis

36. Acknowledgments Microarray collaborators Ed Stone Val Sheffield Jian Huang Kwang-youn Kim Ruth Swiderski Kevin Knudtson Rod Philp CBCB Todd Scheetz Tom Casavant Terry Braun Nathan Schulz

37. Example Studies • Physicochemical modeling of cell signaling pathways. B.B. Aldridge et al. Nature Cell Biology. 8(11) Nov 2006. 1195-1203. • Reverse engineering of regulatory networks in human B cells. K. Basso. Nature Genetics. 37(4) Apr 2005. 382-397. • Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins. A. Sigal. Nature Methods. 3(7) Jul 2006. 525-532. • Structural systems biology: modeling protein interactions. P. Aloy. Nature Reviews. Mar 2006. 188-198 .

38. Reverse engineering of regulatory networks in human B cells • Have lots of microarrays, how can you reconstruct the network of regulation. • Lower organisms, works • Higher, too much noise • ARACNe algorithm for the reconstruction of accurate cellular networks • Find correlated genes • Remove indirect correlations

39. Mutual Information • How much does value t1 tell you about value t2 • If MI = 0 there is no information if MI = 1 you have perfect information. • Similar to correlation coefficient but able to capture more complex interactions.

40. Find direct interactions • Use “data transmission theory” • Data processing inequality (DPI) • If (x,y) and (y,z) directly interact and (x,z) indirectly interact • Mutual information of x,z will be less than x,y or y,z • High MI values confound analysis • Three member loops are common, and difficult to parse.

41. Assessing validity and coverage

43. Validation and conclusions • Validated 34 candidates by chip-chip • Make conclusions about hierarchical nature of the myc network • Know important members of the network for further study.

44. Dynamic proteomics in individual human cells uncovers widespread cell-cycle dependence of nuclear proteins • Measure temporal and spatial relations in dividing cells of 20 fluorescently labeled proteins.

45. Keys • New technique to introduce a fluorescent label that does not perturb the protein function (as much) • In-silico synchronization

49. Results of the paper: • Large number of proteins that probably are involved in cell cycle control • A general, scalable technique for studying location and interaction of proteins.