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Network Biology BMI 730

Network Biology BMI 730. Kun Huang Department of Biomedical Informatics Ohio State University. Understanding!. Systems Sciences Theory Analysis Modeling Synthesis/prediction Simulation Hypothesis generation. Prediction!. Systems Biology. Biology Domain knowledge Hypothesis testing

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Network Biology BMI 730

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

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

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

  4. 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 ] • Réka Albert and Albert-László Barabási, Statistical mechanics of complex networks Review of Modern Physics 74, 47-97 (2002). [ PDF ] [cond-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 ] • R. Albert, H. Jeong, and A.-L. Barabási, Error and attack tolerance in complex networksNature 406 , 378 (2000). [ PDF ] [ cond-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 ] • H. Jeong, S. Mason, A.-L. Barabási and Zoltan N. Oltvai, Lethality and centrality in protein networksNature 411, 41-42 (2001). [ PDF ] [ Supplementary Materials  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 ] • 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 ] • Réka Albert and Albert-László Barabási, Topology of evolving networks: Local events and universality Physical Review Letters 85, 5234 (2000). [ PDF ] [ cond-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 ]

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

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

  7. 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) .

  8. R Milo et al., Science298, 824-827 (2002).

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

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

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

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

  13. 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.

  14. 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 b n=4 n=2 n=1 b/2 X*>>K, F(X*) = b X* = K, F(X*) = b/2 1 2 0 X*/K

  15. Genetic Network – Transcription Network Repressor F(X*) approximates step function (logic) for large n b n=4 n=2 n=1 b/2 1 2 0 X*/K

  16. Genetic Network – Transcription Network • TF factor X regulates protein (gene) Y • Timescale for E. Coli • Binding of signaling molecule to TF and changing its activity ~1msec • Binding of active TF to DNA ~1sec • Transcription + translation of gene ~5min • 50% change of target protein concentration ~1h

  17. Genetic Network – Transcription Network • Logic function approximation • Hill function is for detailed modeling. Logic function is for simplicity and mathematical clarity. q 0 t Activator Repressor K – threshold b – maximal expression level

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

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

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

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

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

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

  24. Motif Statistics and Dynamics • Autoregulation X(t)/K 1 0 1 Time (at) X A mRNA DNA Gene Y

  25. Motif Statistics and Dynamics • Autoregulation X(t)/K 1 0 1 Time (at) Short response time

  26. Motif Statistics and Dynamics • Autoregulation If b fluctuates, Xss is stable for negative autoregulation but not for simple regulation. Robustness / stabilization

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

  28. 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.

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

  30. X Y Y Z Ton Coherent Feed Forward Loop (FFL) Sx Sx X AND Z Sign sensitive delay for ON signal

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

  32. 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.

  33. Coherent Feed Forward Loop (FFL) Timing instrument

  34. X Y Y Z Sx Sy X AND Z Coherent Feed Forward Loop (FFL) Nature Genetics31, 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

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

  36. 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.

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

  39. Table 3.Summary of functions of the FFLs * In incoherent FFL with basal level, Sy modulates Z between two nonzero levels. Mangan, S. and Alon, U. (2003) Proc. Natl. Acad. Sci. USA 100, 11980-11985

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

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

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

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

  44. 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

  45. Genotype-Phenotype Network Wu et al.Molecular Systems Biology, 2009 4:189, Network-based global inference of human disease genes

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

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

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