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Albert Laszlo Barabasi - Innovation inspired positive change in health care
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Albert Laszlo Barabasi - Innovation inspired positive change in health care

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Innovation inspired positive change in health care, by Albert Laszlo Barabasi

Innovation inspired positive change in health care, by Albert Laszlo Barabasi

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Albert Laszlo Barabasi - Innovation inspired positive change in health care Albert Laszlo Barabasi - Innovation inspired positive change in health care Presentation Transcript

  • Network Medicine Albert-László BarabásiCenter for Complex Networks Research Northeastern University Department of Medicine and CCSB Harvard Medical School Central European University, Budapest www.BarabasiLab.com
  • DISEASOME PHENOME GENOME Gene Diseasenetwork network Goh, Cusick, Valle, Childs, Vidal & Barabási, PNAS (2007)
  • Human Disease Network
  • Human Protein-Protein Interaction Network Rual et al. Nature 2005; Stelze et al. Cell 2005
  • World Wide WebExponential Network Nodes: WWW documents Links: URL links Over 10 billion documents ROBOT: collects all URL’s found in a document and follows them recursivelyScale-free Network P(k) ~ k- R. Albert, H. Jeong, A-L Barabási, Nature, 401 130 (1999).
  • Metabolic Network Protein InteractionsJeong, Tombor, Albert, Oltvai, & Barabási, Nature (2000); Jeong, Mason, Barabási &.Oltvai, Nature (2001); Wagner & Fell, Proc. R. Soc. B (2001)
  • Robustness of scale-free networks node failureFailures Attacks Albert, Jeong, Barabási, Nature 406 378 (2000)
  • Hubs and Essentiality 18% 24% 62% Hubs evolve slower: they are more alike in different organisms [H Fraser et al., Science (2002). Krylov, et al. Genome Res.(2003)] Hub removal has more phenotypic consequences [Yu et al. Science (2008)] Jeong, Mason, Barabási, and Oltvai, Nature 411, 41-42 (2001
  • Are Disease Genes Hubs?Goh, Cusick, Valle, Childs, Vidal & Barabási, PNAS (2007)
  • Essential Proteins are Hubs;Disease Genes are at the Network Periphery Goh, Cusick, Valle, Childs, Vidal & Barabási, PNAS (2007)
  • Human Disease Network
  • ~ 13’039’018 patients~ 32’341’348 records (hospitalizations) Identifier, Time of Visit, State, Age, Gender, Poverty (0-1), Up to 10 diagnosis (from ~950 cat (3digit) / ~14000 (5digit) )
  • I1 I2N Disease 1 C12 Disease 2 Cij N Cij N  I i I j  ij  ij  Ii I j I i I j ( N  I i )( N  I j ) Park, Lee, Christakis, Barabási, Mol Syst Biol (2008)
  • Genetic Associations and Comorbidity Measures (RR, or Relative Risk)
  • Average Comorbidity Values Park, Lee, Christakis, Barabási, Mol Syst Biol (2008)
  • Phenotypic Disease Network (PDN) I1 I2N Disease 1 C12 Disease 2 Cij N Cij N  I i I j  ij  ij  Ii I j I i I j ( N  I i )( N  I j ) (RR or Relative Risk) Hidalgo, Blumm, Barabasi & Christakis, PLOS Comp. Biol. (2009)
  • http://hudine.neu.edu/Hidalgo, Bloom, A-L. B., Christakis, PLOS Comp. Biol. (2009); A. Rzhetsky , PNAS (2007)
  • Network Position and Survival Rate  Hidalgo, Blumm, Barabasi & Christakis, PLOS Comp. Biol. (2009)
  • CONTROLLABILITY A system is controllable if it can be driven from any initial state to any desired final state in finite time. R. E. Kalman, J.S.I.A.M. Control (19
  • FuturICT: Taming Complexity BarabasiLab.com
  • CONTROLLABILITY: What did we learn? Organizational Network: 1-10% Regulatory Network: 80% • Driver nodes avoid the hubs. • The more interconnected a network (high <k>), the fewer driver nodes we need. • The more uniform the node degrees, the fewer driver nodes we need. • Sparse and heterogeneous networks are hardest to control (i.e. most real networks). Y.-Y. Liu, J.-J. Slotine, A.-L. Barabási, Nature (20
  • Zoom
  • Calling Patterns at Midnight and Noon Busy@Midnight Sleep@NoonSleep@Midnight Busy@Noon Midnight Noon
  • Human Protein-Protein Interaction Network Rual et al. Nature 2005; Stelze et al. Cell 2005
  • Local clustering of disease genes: disease modules Cellular components that form a topological module have have closely related function, thus they correspond to a function module, and a disease is a result of the breakdown of functional module Human Interactome AsthmaNodes color correspond to different diseases fromOMIM/GWAS studies
  • Local clustering of disease genes: disease modules Cellular components that form a topological module have have closely related function, thus they correspond to a function module, and a disease is a result of the breakdown of functional module Human Interactome AsthmaNodes color correspond to different diseases fromOMIM/GWAS studies
  • Genes that are involved in the same disease show a high propensity to interact with each other Each axis represent the category of disease associated with the protein in an interaction pair Gandhi et al Nat Genet. 2006, :285-93Goh, Cusick, Valle, Childs, Vidal & Barabási, PNAS (2007) OMIM disease genes and clustering
  • Mapping out disease modules Barabási, Gulbahce, Loscalzo (2010)
  • Interactome ReconstructionData Links Nodes Experimental/Predictive Metabolic 5287 RegulatoryBinary Interaction Experimental/(BinaryNetwork (binary 1280 considered from Intact andinteractions from Mint database)Y2Hybrid, Intact and Mint) Literature curated Kinase 42420 15315 6101 CORUM 1620 4197 2936 BinaryBinary Interaction 27837 172 7190 3302 Experimental/Binary 12775network Y2Hhybrid only 403 1929Transfac regulatory 1340 781 ExperimentalnetworkMetabolic couplingInteraction network 10642 921 Experimental/ Predictive(BIGG/KEGG)Curated HPRD-Biogrid- 74195 10890 Literature curatedIntact-MintCORUM-All complexes 31276 2069 ExperimentaldataKinase-substrate pairs 327 (kinases) 6110 Experimental/Literature(PhosphoSitePlus, 1771 (substrates)Phospho.ELM)
  • Seed Genes: Disease genes associated with asthma Sources Genes References (PubMed id) FCER1B,HLA-DRB1,HLA-DQB1,HLA- DPB1,STAT6,NR3C1,GFRA2,GATA2,SH2B3,IKZF2,GST Literature, as compiled in Vercelli et al M1,GSTP1,GSTT1,FLG,IL10,IL18,CTLA4,HAVCR1,GPR 2008 A,NAT2,NOS1,CMA1,ACE,TBXA2R,CTA,SCGB1A1 18301422 CCL17,CCL22,CCL2,CD200,CX3CR1,CXCR1,CXCR2,IL8, Chemokines CCL11, CCL11TNFRSF4TNFSF4ICOSC5CHRNA3TRPA1TRPV1H Chemokines RH4 Co-stim pathway TNFRSF4,TNFSF4,ICOS Complement pathway C5 GPCR-ligand inflammatory mediators CHRNA3,TRPA1,TRPV1,HRH4,PTGDS,GPR44,HNMT,P pathway TGER2 Growth factors pathway F2RL1,FGF2,PDGFB CSF2,IKBKB,NGF,PDE4,PIK3CA,STAT1,MAPK14,MAPK Inflammatory signaling pathway 8,MMP12,SYK,IRAK3,OSM,TNFA ALOX5AP,LTB4R2,LTA4H,LTC4S,ALOX5,IFNB1,TLR7,T Leukotriene pathway LR3,TLR9,TLR4 Pathogen response pathway IFNB1,TLR7,TLR3,TLR9,TLR4 Proteases pathway MMP9,TPSAB1,SPINK5,ELANE Structural Genes/Mucosal EpithelialAltogether: pathway CTNNA3,MUC7,TGFB1,CLCA1,MUC5AC Th17 pathway IL17,IL23A,IL6145 genes Th2 pathway IL13RA2,IL4,IL33,IL4R,IL9,IL25 ADORA2A,SRC,CD28,IL21,IL22,EGF,CD40 EDN1,GSDMA,GSDMB,HLA- 21103062,20860503,20622879,2015 DQA1,IL18R1,IL2RB,MAVS,MYB,PDE11A,PDE4D,RAD 9242,19198610,20920776,19426955 50,RORA,SCG3,SLC22A5,SMAD3,VDR,IL13,CD14,DPP ,20159242,20159242,20881960,202 GWAS-Asthma 10,ORMDL3,IL12B,PTGDR 08534,17611496 GWAS-Childhood Asthma CRB1,DENND1B,CHCHD9,TLE4,ADAM33,CCL5 20032318,19714205,21103062 GWAS-Plasma eosinophil count IL1RL1,IL5,TSLP 19198610 GWAS-YKL-40 levels CHI3L1 18403759 ADRB2,ALOX5,IGHE,HNMT,MUC7,PHF11,SCGB3A2,T BX21,SCGB1A1,CCL11TNFA,LTA4H,LTC4S,IL4,IL33,IL4 R,IL9,TLR9,SPINK5,CTNNA3,CRHR1,IL8TLR4CLCA1,M MeSH term associated with asthma UC5AC,IL25 CCR3,GPR44,PTGER2,IRAK3,ASRT3,ASRT4ASRT6HLA- G,PLA2G7ADRB2IGHEALOX5,HNMT,MUC7,PHF11,SC OMIM genes realeated to asthma GB3A2,TBX21,SCGB1A1,CCL11,TNFA
  • Mapping out disease modules Barabási, Gulbahce, Loscalzo (2010)
  • Seed genes clusters in Human Interactome Of the 145 seed genes: 126 on the interactome. 37: giant component. 77: isolated
  • Disease Module Detection
  • Mapping out disease modules Barabási, Gulbahce, Loscalzo (2011)
  • Biological data for validation Differentially expressed gene set Gene GSE3183 (human airway Ontologies(biological hyperresponsiveness pathway)/ MSIgDB GSE473 (Human CD4+ MSIgDB-Molecular signature lymphocytes) database: GeneGo pathways forGSE470 (Human asthma 6700 gene sets containing Asthma exacerbatory data from factors)GSE3004 KEGG, Biocarta, canonical (Human airway and reactome pathways 35 pathways with 737 genes Genes associated with to be significantly enriched epithelial) Diseases comorbid GSE2125 (alveolar for asthma with Asthma (493) macrophages; p<0.02) Genes associated with and human primary celllines of NHBE, NHLF and Superarray diseases comorbid to asthma with relative risk relative risk BSMC, exposed to pathways (RR) score >1.5 –Medicare cytokines or PBS 38 pathways specific to data (p<0.05) asthma Genes associated with diseases comorbid to asthma with relative risk relative risk (RR) score >2 –I3 data
  • Validation of the predicted disease genesDo the predicted genes show a statisticallysignificant biological association with asthma?Can we define the disease module boundary?
  • Biological validation of prioritized DMD genes Over all statistical significance appear to be limited to roughly to the first 200 genes selected by the method. Beyond these genes the statistical significance gradually vanishes, indicating that the genes later added may not be part of the disease module
  • Final Candidate Genes Prioritization
  • GeneGo pathways and 200 prioritized candidate genes Community 1 enriched in most of pathways
  • Network Medicine Barabási, New England Journal of Medicine (2007)