Do not reproduce without permission 1 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Regulatory networks [Horak, et al, Genes & Development, 16: ] Do not reproduce without permission 2 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Protein Interaction Network [Jeong et al.] Do not reproduce without permission 3 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Metabolic networks [DeRisi, Iyer, and Brown, Science, 278: ] Do not reproduce without permission 4 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Combining networks forms an ideal way of integrating diverse information Metabolic pathway Transcriptional regulatory network Physical protein- protein Interaction Co-expression Relationship Part of the TCA cycle Genetic interaction (synthetic lethal) Signaling pathways Do not reproduce without permission 5 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Toward Systematic Ontologies for Function, using Networks General Networks [Eisenberg et al.] Do not reproduce without permission 6 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Networks occupy a midway point in terms of level of understanding 1D: Complete Genetic Partslist ~2D: Bio-molecular Network Wiring Diagram 3D: Detailed structural understanding of cellular machinery [Jeong et al. Nature, 41:411] [Fleischmann et al., Science, 269 :496] Do not reproduce without permission 7 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Networks as a universal language Disease Spread [Krebs] Protein Interactions [Barabasi] Social Network Food Web Neural Network [Cajal] Electronic Circuit Internet [Burch & Cheswick] 8 UTILIZING PROTEIN CRYSTAL STRUCTURES, WE CAN DISTINGUISH THE DIFFERENT BINDING INTERFACES Source: Kim et al. Science (2006) ILLUSTRATIVE PDB Map all interactions to available homologous structures of interfaces Distinguish overlapping from non- overlapping interfaces _Groupmeeting_PMK 9 POSITIVE SELECTION LARGELY TAKES PLACE AT THE NETWORK PERIPHERY Source: Nielsen et al. PLoS Biol. (2005), HPRD, and Kim et al. PNAS (2007) High likelihood of positive selection Lower likelihood of positive selection Not under positive selection No data about positive selection Positive selection in the human interactome _Groupmeeting_PMK 10 HDAC SMRT CtBPCIR PSEN Hariless NCSTNAPH-1 CSL MAML HATs SKIP Hes1/5 PreT Serrate DeltaNotch FringeDvl Numb Deltex PSE2 Groucho TACE -Secretase complex Ras/MAPK MAPK signaling pathway Gene expression S2 DLLT DLG1^5 DLK1 LCK^3 GSK3B CNTN1 CTNNB1 EPS8^20 AP2A^11 ITCH ABL1^2 APP APBA1^27 BXW7 CSNK2A2 RBPMS^2 MDM2 CD8A^83 THRB^5 G22P1 TCF8^27 TLE1^3 FOXG1B^2 AHR^13 HIC1^9 AR^9 ESR1^6 DDB1^16 ARIE^100 EVI1^2 HD TP53^2 LEF1 GRB2^10 RELA YY1 PCAF SMAD3 CSNK2A1 CSHL1 SND1^2RBL2 MYOD1 NR3C1 PML^2 NFKB1 RBL1 SMAD2 HNRPU Classical v High-throughput: Core v Extended Interactions [Lu et al. TIG (2007)] _Groupmeeting_PMK 11 THERE IS A RELATIONSHIP BETWEEN NETWORK TOPOLOGY AND GENE EXPRESSION DYNAMICS Source: Han et al. Nature (2004) and Yu*, Kim* et al. PLOS Comp. Bio. (2007) Frequency Co-expression correlation _Groupmeeting_PMK 12 Determination of "Level" in Regulatory Network Hierarchy with Breadth-first Search [Yu et al., PNAS (2006)] _Groupmeeting_PMK 13 Example of Path Through Regulatory Network [Yu et al., PNAS (2006)] Expression of MOT3 is activated by heme and oxygen. Mot3 in turn activates the expression of NOT5 and GCN4, mid-level hubs. GCN4 activates two specific bottom- level TFs, Put3 and Uga3, which trigger the expression of enzymes in proline and nitrogen utilization. _Groupmeeting_PMK 14 Yeast Regulatory Hierarchy [Yu et al., PNAS (2006)] _Groupmeeting_PMK 15 Yeast Network Similar in Structure to Government Hierarchy with Respect to Middle-managers _Groupmeeting_PMK 16 Bottleneck bridging between processes [Yu et al. PLOS CB (2007)] _Groupmeeting_PMK 17 Bottlenecks & Hubs [Yu et al., PLOS CB (2007)] _Groupmeeting_PMK 18 Target Genes Transcription Factors 142 transcription factors 3,420 target genes 7,074 regulatory interactions From integrating data from Snyder, Young, Kepes, and TRANSFAC Yeast Regulatory Network: a platform for integration [Yu et al (2003), TIG] _Groupmeeting_PMK 19 Classification of biological networks DirectedUndirected ExpressionRegulationInteraction Metabolism _Groupmeeting_PMK 20 Classical v High-throughput Representations: Issues and Differences [Lu et al. TIG (2007, in press)] _Groupmeeting_PMK 21 t-SNAREs v-SNAREs H + -transporting ATPase (vacuolar) lipid biosynthesis cytochrome c oxidase cytochrome bc1 complex oligosaccharyltransferase transport COPII carbohydrate transport protein targeting amino acid glycosylation Figure 6: A map of known and a subset of predicted interactions among helical membrane proteins. Nodes represent helical membrane proteins, and edges represent interactions among them. Red edges represent known interactions that are also predicted to interact, blue edges represent other known interactions, and green edges represent ~700 top interaction predictions (ranked by descending logistic regression score) out of a total of 4,145. Purple nodes represent helical membrane proteins that show up in the known interactions, and green nodes represent new helical membrane proteins. Map of Known and Predicted Membrane Protein Interactome in Yeast New KnownKnown Xia et al. JMB (2006) _Groupmeeting_PMK 22 Network usage under different conditions Cell cycleSporulationDiauxic shiftDNA damageStress Luscombe et al. Nature 431: 308 Do not reproduce without permission 23 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu BioNets: Selecting Structural Targets Based on Networks (nesg.org) Individual Proteins Protein Target Families Network between Family Members Given network (determined genetically or predicted computationally) : Target proteins at critical positions (e.g. hubs) for structure determination Use structures to rationalize interactions in network Solve complexes to verify interactions and provide Gold-Standards for further extrapolation [G Montelione] Do not reproduce without permission 24 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Multi-stage conditions have: longer path lengths more inter-regulation between TFs Divide cell cycle into parts Phase specific (transient) hubs and network rewiring during cell cycle Do not reproduce without permission 25 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu transcription factors used in cell cycle phase specificubiquitous 2. parallel inter-regulation Regulatory circuitry of cell cycle time-course Do not reproduce without permission 26 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu VisualComplexity.com Do not reproduce without permission 27 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Scale-free networks in Biology Hubs dictate the structure of the network log(Degree) log(Frequency) Power-law distribution [Barabasi] Do not reproduce without permission 28 Gerstein.info/talks (c) (c) Mark Gerstein, 2002, Yale, bioinfo.mbb.yale.edu Toward Systematic Ontologies for Function, using Networks General Networks [Eisenberg et al.] Hierarchies & DAGs [Enzyme, Bairoch; GO, Ashburner; MIPS, Mewes, Frishman] Interaction Vectors [Lan et al, IEEE 90:1848]