1. Network Biology BMI 730 Kun Huang Department of Biomedical Informatics Ohio State University

2. Biology Domain knowledge Hypothesis testing Experimental work Genetic manipulation Quantitative measurement Validation Systems Sciences Theory Analysis Modeling Synthesis/prediction Simulation Hypothesis generation Informatics Data management Database Computational infrastructure Modeling tools High performance computing Visualization Systems Biology Understanding! Prediction!

3. Review of Network Topology – Scale Free and Modularity Elements of Dynamical Modeling Network Motif Analysis Integration of Multiple Networks – Several Examples Course Projects

5. A Tale of Two Groups A.-L. Barabasi at University of Notre Dame Ten Most Cited Publications: Albert-László Barabási and Réka Albert, Emergence of scaling in random networks, Science 286, 509- 512 (1999). [ PDF ] [ cond-mat/9910332 ]PDFcond-mat/9910332 Réka Albert and Albert-László Barabási, Statistical mechanics of complex networks Review of Modern Physics 74, 47-97 (2002). [ PDF ] [cond-mat/0106096 ]PDFcond-mat/0106096 H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.-L. Barabási, The large-scale organization of metabolic networks, Nature 407, 651-654 (2000). [ PDF ] [ cond-mat/0010278 ]PDFcond-mat/0010278 R. Albert, H. Jeong, and A.-L. Barabási, Error and attack tolerance in complex networks Nature 406, 378 (2000). [ PDF ] [ cond-mat/0008064 ]PDFcond-mat/0008064 R. Albert, H. Jeong, and A.-L. Barabási, Diameter of the World Wide Web Nature 401, 130-131 (1999). [ PDF ] [ cond-mat/9907038 ]PDFcond-mat/9907038 H. Jeong, S. Mason, A.-L. Barabási and Zoltan N. Oltvai, Lethality and centrality in protein networks Nature 411, 41-42 (2001). [ PDF ] [ Supplementary Materials 1, 2 ] PDF 1, 2 E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A.-L. Barabási, Hierarchical organization of modularity in metabolic networks, Science 297, 1551-1555 (2002). [ PDF ] [ cond-mat/0209244 ] [ Supplementary Material ]PDFcond-mat/0209244 Supplementary Material A.-L. Barabási, R. Albert, and H. Jeong, Mean-field theory for scale-free random networks Physica A 272, 173-187 (1999). [ PDF ] [ cond-mat/9907068 ]PDFcond-mat/9907068 Réka Albert and Albert-László Barabási, Topology of evolving networks: Local events and universality Physical Review Letters 85, 5234 (2000). [ PDF ] [ cond-mat/0005085 ]PDFcond-mat/0005085 Albert-László Barabási and Zoltán N. Oltvai, Network Biology: Understanding the cells's functional organization, Nature Reviews Genetics 5, 101-113 (2004). [ PDF ]PDF

6. Power Law Small World Rich Get Richer (preferential attachment) Self-similarity HUBS!

7. Modularity Scale-free and Modularity/Hierarchy are thought to be exclusive. Scale-free (a) Modular (b)

8. Subgraphs Subgraph: a connected graph consisting of a subset of the nodes and links of a network Subgraph properties: n: number of nodes m: number of links (n=3,m=3) (n=3,m=2) (n=4,m=4) (n=4,m=5).

9. R Milo et al., Science 298, 824-827 (2002).

10. Review of Network Topology – Scale Free and Modularity Elements of Dynamical Modeling Network Motif Analysis Integration of Multiple Networks – Several Examples Course Projects

11. Genetic Network – Transcription Network Regulation of protein expression is mediated by transcription factors DNA Promoter Gene Y DNA RNA polymerase Gene Y mRNA Protein Y Transcription Translation

12. Genetic Network – Transcription Network TF factor X regulates protein (gene) Y DNA Gene Y mRNA Protein Y X* X SXSX Y Y Y Y Y Y X  Y Activation / positive control, X is called activator.

13. Genetic Network – Transcription Network TF factor X regulates protein (gene) Y DNA Gene Y mRNA X Y Y Y Y DNA Gene Y X* X No transcription Repression / negative control, X is called repressor. X Y

14. Genetic Network – Transcription Network How to model the input-output relationship? Concentration of active TF X* Rate of production of protein Y Concentration of protein Y F(X*) is usually monotonic, S-shaped function.

15. Genetic Network – Transcription Network Hill function Derived from the equilibrium binding of the TF to its target site. Activator K – activation coefficient  – maximal expression level n – Hill coefficient (1<n<4 for most cases) F(X*) approximates step function (logic) for large n X*>>K, F(X*) =  X* = K, F(X*) =  /2 X*/K n=1 n=4 n=2 0 12  

16. Genetic Network – Transcription Network Repressor X*/K n=1 n=4 n=2 0 12   F(X*) approximates step function (logic) for large n

17. Genetic Network – Transcription Network TF factor X regulates protein (gene) Y Timescale for E. Coli 1.Binding of signaling molecule to TF and changing its activity~1msec 2.Binding of active TF to DNA~1sec 3.Transcription + translation of gene~5min 4.50% change of target protein concentration ~1h

18. Genetic Network – Transcription Network Logic function approximation Hill function is for detailed modeling. Logic function is for simplicity and mathematical clarity. Activator K – threshold  – maximal expression level Repressor t0 

19. Genetic Network – Transcription Network Logic function approximation Multiple input X* AND Y* X* OR Y* SUM

20. Genetic Network – Transcription Network The dynamics Change over time Degradation Dilution (cell growth and volume increase) Response time (characteristics) Dynamical equation Equilibrium (steady state)

21. Genetic Network – Transcription Network The dynamics Response time (characteristics) Sudden removal of production 1 0.5

22. Genetic Network – Transcription Network The dynamics Response time (characteristics) Sudden initiation of production 1 0.5

23. Motif Statistics and Dynamics Autoregulation Self-edge in the transcription network

24. Motif Statistics and Dynamics Autoregulation DNA Gene Y mRNA X A Negative autoregulation

25. Motif Statistics and Dynamics Autoregulation DNA Gene Y mRNA X A 10 Time (  t) X(t)/K 1

26. Motif Statistics and Dynamics Autoregulation 1 0 Time (  t) X(t)/K 1 Short response time

27. Motif Statistics and Dynamics Autoregulation Robustness / stabilization If  fluctuates, X ss is stable for negative autoregulation but not for simple regulation.

28. Review of Network Topology – Scale Free and Modularity Elements of Dynamical Modeling Network Motif Analysis Integration of Multiple Networks – Several Examples Course Projects

29. Motif Topology Each edge has 4 choices (why?). Three edges 4X4X4 = 64 choices. There are symmetry redundancy. Despite the choices of activation and repression, there are 13 types.

30. X Y Z X Y Z X Y Z X Y Z X Y Z X Y Z X Y Z X Y Z Coherent Feed Forward Loop (FFL) Incoherent Feed Forward Loop

31. Coherent Feed Forward Loop (FFL) X Y Z X Y Z AND SxSx T on Sign sensitive delay for ON signal SxSx

32. Coherent Feed Forward Loop (FFL) X Y Z X Y Z AND SxSx Sign sensitive delay for ON signal SxSx

33. Coherent Feed Forward Loop (FFL) The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks Mangan, S.; Zaslaver, A.; Alon, U. J. Mol. Biol., 334:197-204, 2003.

34. Coherent Feed Forward Loop (FFL) Timing instrument

35. Coherent Feed Forward Loop (FFL) X Y Z X Y Z AND SxSx SySy Nature Genetics 31, 64 - 68 (2002) Network motifs in the transcriptional regulation network of Escherichia coli Shai S. Shen-Orr, Ron Milo, Shmoolik Mangan & Uri Alon Noise (low-pass) filter

36. Coherent Feed Forward Loop (FFL) X Y Z X Y Z OR SxSx Sign sensitive delay for OFF signal SxSx

37. Coherent Feed Forward Loop (FFL) A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coli Shiraz Kalir, Shmoolik Mangan and Uri Alon, Mol. Sys. Biol., Mar.2005.

38. Coherent Feed Forward Loop (FFL) A coherent feed-forward loop with a SUM input function prolongs flagella expression in Escherichia coli Shiraz Kalir, Shmoolik Mangan and Uri Alon, Mol. Sys. Biol., Mar.2005.

39. Incoherent Feed Forward Loop (FFL) X Y Z X Y Z AND SxSx Fast response time to steady state SxSx

40. Table 3. Summary of functions of the FFLs * In incoherent FFL with basal level, Sy modulates Z between two nonzero levels. Steady-state logic is sensitive to both Sx and Sy Coherent and incoherent * Types 1, 2 AND Types 3, 4 OR Sign-sensitive delay upon Sx stepsCoherentTypes 1, 2, 3, 4 Sy-gated pulse generator upon Sx steps Incoherent with no basal Y level Types 3, 4 AND Types 1,2 OR Sign-sensitive acceleration upon Sx steps Incoherent with basal Y level Types 1,2,3,4 Mangan, S. and Alon, U. (2003) Proc. Natl. Acad. Sci. USA 100, 11980-11985

41. Review of Network Topology – Scale Free and Modularity Elements of Dynamical Modeling Network Motif Analysis Integration of Multiple Networks – Several Examples Course Projects

42. Barabasi A-L, Network medicine – from obesity to “Diseasome”, NEJM, 357(4): 404- 407, 2007. Integration of Multi-Modal Data

43. Tissue-Tissue Network Dobrin et al. Genome Biology 2009 10:R55 doi:10.1186/gb-2009-10-5-r55

44. Tissue-Tissue Network Dobrin et al. Genome Biology 2009 10:R55 doi:10.1186/gb-2009-10-5-r55

45. Genotype-Phenotype Network Scoring scheme of CIPHER. First, the human phenotype network, protein network, and gene– phenotype network are assembled into an integrated network. Then, to score a particular phenotype– gene pair (p, g), the phenotype similarity profile for p is extracted and the gene closeness profile for g is computed from the integrated network. Finally, the linear correlation of the two profiles is calculated and assigned as the concordance score between the phenotype p and the gene g. Wu et al. Molecular Systems Biology, 2009 4:189, Network-based global inference of human disease genes

46. Genotype-Phenotype Network Known disease gene Rank in 8919 candidates CIPHER-SP%CIPHER-DN% BRCA110.0120.02 AR30.033 ATM190.2140.04 CHEK2660.74190.21 BRCA21391.56490.54 STK111501.69210.23 RAD511742.00360.40 PTEN1882.10240.26 BARD11962.20410.45 TP532873.22450.50 RB1CC17988.95636071.30 NCOA397310.913433.84 PIK3CA164418.433674.11 PPM1D194621.82731882.04 CASP8497855.81239726.87 TGF1711679.78350239.26 Wu et al. Molecular Systems Biology, 2009 4:189, Network-based global inference of human disease genes

49. Kelley and Ideker, Nature Biotechnology, 2005 23:561-566, Systematic interpretation of genetic interactions using protein networks

50. Review of Network Topology – Scale Free and Modularity Elements of Dynamical Modeling Network Motif Analysis Integration of Multiple Networks – Several Examples Course Projects