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Pathways, Networks and Systems Biology BMI 730

Pathways, Networks and Systems Biology BMI 730. Kun Huang Department of Biomedical Informatics Ohio State University. Pathways and Networks Databases and Resources Challenges in system biology Large data New computation and modeling methods Kinetics vs. dynamics Scale-Free Network

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Pathways, Networks and Systems Biology BMI 730

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  1. Pathways, Networks and Systems BiologyBMI 730 Kun Huang Department of Biomedical Informatics Ohio State University

  2. Pathways and Networks • Databases and Resources • Challenges in system biology • Large data • New computation and modeling methods • Kinetics vs. dynamics • Scale-Free Network • Network Motifs

  3. Functions, pathways and networks Genes

  4. Pathway – What’s out there? 240

  5. Pathway software • Pathway Miner (Free, need to compile) • GenMapp (Free) • CytoScape (Free) • GESA (Free) • DAVID (Free) • Pathway Architect (Commercial) • Pathway Studio (Commercial) • Ingenuity Pathway Analysis (Commercial) • Manually curated • On-demand computation

  6. Understanding! • System Sciences • Theory • Analysis • Modeling • Synthesis/prediction • Simulation • Hypothesis generation Prediction! System 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

  7. “A key element of the GTL program is an integrated computing and technology infrastructure, which is essential for timely and affordable progress in research and in the development of biotechnological solutions. In fact, the new era of biology is as much about computing as it is about biology. Because of this synergism, GTL is a partnership between our two offices within DOE’s Office of Science—the Offices of Biological and Environmental Research and Advanced Scientific Computing Research. Only with sophisticated computational power and information management can we apply new technologies and the wealth of emerging data to a comprehensive analysis of the intricacies and interactions that underlie biology. Genome sequences furnish the blueprints,technologies can produce the data, and computing can relate enormous data sets to models linking genome sequence to biological processes and function.”

  8. Open Loop Output Input Control System Closed Loop Output Input Control System + ± Feedback is ubiquitous; it is essential for the stabilization of any system (biological, engineering, social …) Feedback

  9. Taniguchi et al.Nature Reviews Molecular Cell Biology7, 85–96 (February 2006) | doi:10.1038/nrm1837

  10. Challenges in system biology • Large data • Kinetics vs. dynamics • Multiple (temporal) scale • New computation and modeling methods • New mathematics or new physics laws

  11. Oscillation A B Maeda et al., Science, 304(5672):875-878, 2004

  12. Simple Two Nodes Pattern Chang et al., Multistable and multistep dynamics in neutrophil differentiation, BMC Cell Biology 2006, 7:11 Bistable dynamics in a two-gene system with cross-regulation. A. Gene regulatory circuit diagram. Blunt arrows indicate mutual inhibition of genes X and Y. Dashed arrows indicate a basal synthesis (affected by the inhibition) and an independent first-order degradation of the factors. B. Two-dimensional XY phase plane representing the typical dynamics of the circuit. Every point (X, Y) represents a momentary state defined by the values of the pair X, Y. Red arrows are gradient vectors indicating the direction and extent that the system will move to within a unit time at each of the (X, Y) positions. Collectively, the vector field gives rise to a "potential landscape", visualized by the colored contour lines (numerical approximation). In this "epigenetic landscape", the stable states (attractors) are in the lowest points in the valleys: a (X>>Y) and b (Y>>X) (gray dots). C. Schematic representation of the epigenetic landscape as a section through a and b in which every red dot represents a cell. Experimentally, this bistability is manifested as a bimodal distribution in flow cytometry histograms in which the stable states a and b appear as peaks at the respective level of marker expression (e.g., Y).

  13. Marlovits et.al., Biophysical Chemistry, Vol:72, p.169-184

  14. Pomerening et.al., Cell, Vol:122(4), p.565-578

  15. New system biology • Kinetics vs. Dynamics • Compartmentalization (Spatial and Temporal) • Hybrid Systems and System Abstraction • Hierarchical/multiscale description • Discrete Event System • New System Theory • Graph Theory and Network Theory / New Mathematics and New Physics

  16. A Tale of Two Groups A.-L. Barabasi Ten Most Cited Publications: • Albert-László Barabási and Réka Albert, Emergence of scaling in random networks , Science286, 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 networksNature406 , 378 (2000). [ PDF ] [ cond-mat/0008064 ] • R. Albert, H. Jeong, and A.-L. Barabási, Diameter of the World Wide Web Nature401, 130-131 (1999). [ PDF ] [ cond-mat/9907038 ] • H. Jeong, S. Mason, A.-L. Barabási and Zoltan N. Oltvai, Lethality and centrality in protein networksNature411, 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, Science297, 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 ]

  17. A Tale of Two Groups Uri Alon at Weissman Institute Selected Publications: R Milo, S Itzkovitz, N Kashtan, R Levitt, S Shen-Orr, I Ayzenshtat, M Sheffer & U Alon, Superfamilies of designed and evolved networks, Science, 303:1538-42 (2004). Pdf. R Milo, S Shen-Orr, S Itzkovitz, N Kashtan, D Chklovskii & U Alon, Network Motifs: Simple Building Blocks of Complex Networks, Science, 298:824-827 (2002). Pdf. S Shen-Orr, R Milo, S Mangan & U Alon, Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31:64-68 (2002). Pdf. S. Mangan, S. Itzkovitz, A. Zaslaver and U. Alon, The Incoherent Feed-forward Loop Accelerates the Response-time of the gal System of Escherichia coli. JMB, Vol 356 pp 1073-81 (2006). Pdf. S Mangan & U Alon, Structure and function of the feed-forward loop network motif. PNAS, 100:11980-11985 (2003). Pdf. S. Mangan, A. Zaslaver and U. Alon, The Coherent Feedforward Loop Serves as a Sign-sensitive Delay Element in Transcription Networks. JMB, Vol 334/2 pp 197-204 (2003). Pdf. Guy Shinar, Erez Dekel, Tsvi Tlusty & Uri Alon, Rules for biological regulation based on error minimization,PNSA. 103(11), 3999-4004 (2006). Pdf. Alon Zaslaver, Avi E Mayo, Revital Rosenberg, Pnina Bashkin, Hila Sberro, Miri Tsalyuk, Michael G Surette & Uri Alon, Just-in-time transcription program in metabolic pathways, Nature Genetics 36, 486 - 491 (2004). Pdf. U. Alon, M.G. Surette, N. Barkai, S. Leibler, Robustness in Bacterial Chemotaxis, Nature 397,168-171 (1999). Pdf M Ronen, R Rosenberg, B Shraiman & U Alon, Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics.PNAS, 99:10555–10560 (2002). Pdf. N Rosenfeld, M Elowitz & U Alon, Negative Autoregulation Speeds the Response Times of Transcription Networks,JMB, 323:785-793 (2002). Pdf. N Rosenfeld & U Alon, Response Delays and the Structure of Transcription Networks,JMB, 329:645–654 (2003). Pdf. S. Kalir, J. McClure, K. Pabbaraju, C. Southward, M. Ronen, S. Leibler, M.G. Surette, U. Alon, Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science, 292:2080-2083 (2001). Pdf Y. Setty, A. E. Mayo, M. G. Surette, and U. Alon, Detailed map of a cis-regulatory input function,PNAS, 100:7702-7707 (2003). Pdf. Shiraz Kalir and Uri Alon, Using a Quantitative Blueprint to Reprogram the Dynamics of the Flagella Gene Network,Cell, 117:713–720, (2004). Pdf.

  18. Expected P(k) ~ k- Found Small world phenomena (http://smallworld.columbia.edu) R. Albert, H. Jeong, A-L Barabasi, Nature, 401 130 (1999).

  19. Other Observations: • Scientific citations • Paper coauthorship/collaboration • Organization structure • Social structure • Actor joint casting in movies • Online communities • Websites linkage • … • Protein networks • Gene networks • Cell function networks • …

  20. Scale-Free Networks

  21. Archaea Bacteria Eukaryotes Metabolic network H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, 407 651 (2000) Organisms from all three domains of life are scale-free networks!

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

  23. Preferential attachment in protein Interaction networks k vs. k : increase in the No. of links in a unit time No PA:k is independent of k PA:k ~k Eisenberg E, Levanon EY, Phys. Rev. Lett. 2003 Jeong, Neda, A.-L.B, Europhys. Lett. 2003

  24. Nature Biotechnology  18, 1257 - 1261 (2000) doi:10.1038/82360 A network of protein−protein interactions in yeast Benno Schwikowski, Peter Uetz & Stanley Fields

  25. Nature Biotechnology  18, 1257 - 1261 (2000) doi:10.1038/82360 A network of protein−protein interactions in yeast Benno Schwikowski, Peter Uetz & Stanley Fields

  26. C. Elegans Drosophila M. Giot et al. Science 2003 Li et al. Science 2004

  27. Consequence 1 : Hubs and Robustness Nature 408 307 (2000) … “One way to understand the p53 network is to compare it to the Internet. The cell, like the Internet, appears to be a ‘scale-free network’.”

  28. Hubs and Robustness 1 node failure S fc 0 1 Fraction of removed nodes, f Complex systems maintain their basic functions even under errors and failures (cell  mutations; Internet  router breakdowns)

  29. Consequence 1 : Hubs and Robustness Complex systems maintain their basic functions even under errors and failures (cell  mutations; Internet  router breakdowns) R. Albert, H. Jeong, A.L. Barabasi, Nature 406 378 (2000)

  30. Achilles’ Heel of complex networks Internet failure attack R. Albert, H. Jeong, A.L. Barabasi, Nature 406 378 (2000)

  31. Yeast protein network - lethality and topological position Highly connected proteins are more essential (lethal)... H. Jeong, S.P. Mason, A.-L. Barabasi, Z.N. Oltvai, Nature 411, 41-42 (2001)

  32. System biology • Integration • Computation • Theory • Prediction!!!

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