Networks and Systems Biology BMI 730 Kun Huang Department of Biomedical Informatics Ohio State University Review of Pathways and Resources Challenges in system biology Large data New computation and modeling methods Kinetics vs. dynamics Scale-Free Network Network Motifs Genes Functions, pathways and networks Pathway Whats out there? 320 Pathway software GenMapp (Free) CytoScape (Free) GESA (Free) DAVID (Free) Pathway Architect (Commercial) Pathway Studio (Commercial) Ingenuity Pathway Analysis (Commercial) Manually curated On-demand computation Review of Pathways and Resources Challenges in system biology Large data New computation and modeling methods Kinetics vs. dynamics Scale-Free Network Network Motifs 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 DOEs 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. Biology Domain knowledge Hypothesis testing Experimental work Genetic manipulation Quantitative measurement Validation System Sciences Theory Analysis Modeling Synthesis/prediction Simulation Hypothesis generation Informatics Data management Database Computational infrastructure Modeling tools High performance computing Visualization System Biology Understanding! Prediction! Feedback is ubiquitous; it is essential for the stabilization of any system (biological, engineering, social ) Control System Input Output Open Loop Control System Input Output Feedback + Closed Loop Taniguchi et al. Nature Reviews Molecular Cell Biology 7, 8596 (February 2006) | doi: /nrm1837 Challenges in system biology Large data Kinetics vs. dynamics Multiple (temporal) scale New computation and modeling methods New mathematics or new physics laws AB Oscillation Maeda et al., Science, 304(5672): , 2004 Simple Two Nodes Pattern 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). Chang et al., Multistable and multistep dynamics in neutrophil differentiation, BMC Cell Biology 2006, 7:11 Marlovits et.al., Biophysical Chemistry, Vol:72, p Pomerening et.al., Cell, Vol:122(4), p 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 Review of Pathways and Resources Challenges in system biology Large data New computation and modeling methods Kinetics vs. dynamics Scale-Free Network Network Motifs A Tale of Two Groups A.-L. Barabasi Ten Most Cited Publications: Albert-Lszl Barabsi and Rka Albert, Emergence of scaling in random networks, Science 286, (1999). [ PDF ] [ cond-mat/ ]PDFcond-mat/ Rka Albert and Albert-Lszl Barabsi, Statistical mechanics of complex networks Review of Modern Physics 74, (2002). [ PDF ] [cond-mat/ ]PDFcond-mat/ H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.-L. Barabsi, The large-scale organization of metabolic networks, Nature 407, (2000). [ PDF ] [ cond-mat/ ]PDFcond-mat/ R. Albert, H. Jeong, and A.-L. Barabsi, Error and attack tolerance in complex networks Nature 406, 378 (2000). [ PDF ] [ cond-mat/ ]PDFcond-mat/ R. Albert, H. Jeong, and A.-L. Barabsi, Diameter of the World Wide Web Nature 401, (1999). [ PDF ] [ cond-mat/ ]PDFcond-mat/ H. Jeong, S. Mason, A.-L. Barabsi and Zoltan N. Oltvai, Lethality and centrality in protein networks Nature 411, (2001). [ PDF ] [ Supplementary Materials 1, 2 ] PDF 1, 2 E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, and A.-L. Barabsi, Hierarchical organization of modularity in metabolic networks, Science 297, (2002). [ PDF ] [ cond-mat/ ] [ Supplementary Material ]PDFcond-mat/ Supplementary Material A.-L. Barabsi, R. Albert, and H. Jeong, Mean-field theory for scale-free random networks Physica A 272, (1999). [ PDF ] [ cond-mat/ ]PDFcond-mat/ Rka Albert and Albert-Lszl Barabsi, Topology of evolving networks: Local events and universality Physical Review Letters 85, 5234 (2000). [ PDF ] [ cond-mat/ ]PDFcond-mat/ Albert-Lszl Barabsi and Zoltn N. Oltvai, Network Biology: Understanding the cells's functional organization, Nature Reviews Genetics 5, (2004). [ PDF ]PDF 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: (2004). Pdf.Pdf R Milo, S Shen-Orr, S Itzkovitz, N Kashtan, D Chklovskii & U Alon, Network Motifs: Simple Building Blocks of Complex Networks, Science, 298: (2002). Pdf.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.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 (2006). Pdf. S Mangan & U Alon, Structure and function of the feed-forward loop network motif. PNAS, 100: (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 (2003). Pdf.Pdf Guy Shinar, Erez Dekel, Tsvi Tlusty & Uri Alon, Rules for biological regulation based on error minimization, PNSA. 103(11), (2006). Pdf.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, (2004). Pdf.Pdf U. Alon, M.G. Surette, N. Barkai, S. Leibler, Robustness in Bacterial Chemotaxis, Nature 397, (1999). PdfPdf M Ronen, R Rosenberg, B Shraiman & U Alon, Assigning numbers to the arrows: Parameterizing a gene regulation network by using accurate expression kinetics. PNAS, 99:1055510560 (2002). Pdf.Pdf N Rosenfeld, M Elowitz & U Alon, Negative Autoregulation Speeds the Response Times of Transcription Networks, JMB, 323: (2002). Pdf. N Rosenfeld & U Alon, Response Delays and the Structure of Transcription Networks, JMB, 329:645654 (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: (2001). PdfPdf Y. Setty, A. E. Mayo, M. G. Surette, and U. Alon, Detailed map of a cis-regulatory input function, PNAS, 100: (2003). Pdf. Shiraz Kalir and Uri Alon, Using a Quantitative Blueprint to Reprogram the Dynamics of the Flagella Gene Network, Cell, 117:713720, (2004). Pdf.Pdf Small world phenomena (http://smallworld.columbia.edu) P(k) ~ k - Found R. Albert, H. Jeong, A-L Barabasi, Nature, (1999). Expected 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 Scale-Free Networks Metabolic network Organisms from all three domains of life are scale-free networks! H. Jeong, B. Tombor, R. Albert, Z.N. Oltvai, and A.L. Barabasi, Nature, (2000) ArchaeaBacteriaEukaryotes Power Law Small World Rich Get Richer (preferential attachment) Self-similarity HUBS! 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 Jeong, Neda, A.-L.B, Europhys. Lett. 2003 Nature Biotechnology 18, (2000) doi: /82360 A network of proteinprotein interactions in yeast Benno Schwikowski, Peter Uetz & Stanley Fields Nature Biotechnology 18, (2000) doi: /82360 A network of proteinprotein interactions in yeast Benno Schwikowski, Peter Uetz & Stanley Fields C. Elegans Li et al. Science 2004 Drosophila M. Giot et al. Science 2003 Nature (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. Consequence 1 : Hubs and Robustness Hubs and Robustness Complex systems maintain their basic functions even under errors and failures (cell mutations; Internet router breakdowns) node failure fcfc 01 Fraction of removed nodes, f 1 S 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 (2000) Achilles Heel of complex networks Internet failure attack R. Albert, H. Jeong, A.L. Barabasi, Nature (2000) 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, (2001) Review of Pathways and Resources Challenges in system biology Large data New computation and modeling methods Kinetics vs. dynamics Scale-Free Network Network Motifs 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). R Milo et al., Science 298, (2002). 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. 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 Coherent Feed Forward Loop (FFL) X Y Z X Y Z AND SxSx T on Sign sensitive delay for ON signal SxSx Coherent Feed Forward Loop (FFL) X Y Z X Y Z AND SxSx Sign sensitive delay for ON signal SxSx 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: , 2003. Coherent Feed Forward Loop (FFL) Timing instrument Coherent Feed Forward Loop (FFL) X Y Z X Y Z AND SxSx SySy Nature Genetics 31, (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 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. 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. 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, Systems biology Integration Computation Theory Prediction!!!