1 Fig. 1 Coronary heart disease related targets from literatures CHD GP IIb /IIIa β 14 CHD DrugBank Table 2 DrugBank Coronary heart dise

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1 * 张燕玲, 黄明峰, 乔延江 ( 北京中医药大学中药信息工程研究室, 北京 ) 2 Intersection IL-6 JAK /STAT 2 coronary heart disease CHD 1 % CHD CHD 1 OMIM Table 1 Coronary heart disease related genes from OMIM database CHD # ADCAD1 # ADCAD2 1 CHD OMIM * VEGFB B * AGTR2 II OMIM * # CHD CHD BAI51B * Tel net # MYOCARDIAL INFARCTION % CHDS8 % CHDS9 % ATHS * NOS CHD 2721

2 1 Fig. 1 Coronary heart disease related targets from literatures CHD GP IIb /IIIa β 14 CHD DrugBank Table 2 DrugBank Coronary heart disease drugs and related targets from DrugBank database ID DB06209 P2Y 12 DB00584 DB00678 II II DB00227 HMG-CoA HMG-CoA α-l DB01020 α-2 DB01115 L α-1c α-1d α-1s α-1h α2 β2 A1 DB06779 III A DB00343 γ-1 DB00457 α-1a α-1b α-1d DB00727 A DB

CHD CHD Reactome 149 CHD 179 CHD 2 Cytoscape2. 8.

2 Coronary heart disease network 8 hub 3-4 hub hub 5 3 3. 1 k P k k 3. 2 Barabasi-Albert BA Fig.

3 CHD CHD Reactome 149 CHD 179 CHD 2 Cytoscape Plugins Advanced Network Merge union 3 CHD Fig. 2 Coronary heart disease network 8 hub 3-4 hub hub k P k k 3. 2 Barabasi-Albert BA Fig. 3 Coronary heart disease drug action network 6 P k ~ K - γ log γ γ < 3 CHD CHD 4 P k = k P k = k P k k log CHD

4 ~ ~ Fig. 6 Path length-frequency statistics figure of CHD disease network upper and drug action network down %

5 CGRP 4 CHD Intersection CGRP 7 CHD CGRP b IL-6 IL-6 IL-6R gp130 CHD 2 JAK1 JAK2 TYK2 JAK /STAT JAK JAK1 JAK2 JAK3 Tyk gp130 IL-6 IL-6 IL-6R gp130 Jak STAT STAT1 STAT3 STAT5 SH2 DNA Bcl-xL Bcl-2 Mcl- 1 CyclinD1 c-myc c-jun Fas VEGF 18 JAK /STAT JAK /STAT 19 Funamoto 20 gp130 STAT3 IL-6 JAK /STAT 7 Fig. 7 The intersection of CHD disease network and drug action IL-6 JAK-STAT network 5 CHD CHD calcitonin gene-related peptide CGRP 8 a CGRP 4 CGRP CHD 14 CGRP CHD CHD 15 CGRP 2725

6 a. b. IL-6 JAK /STAT 8 Fig. 8 The intersection of CHD disease network and medicine network sive resource for 'omics' research on drugs J. Nucleic Acids Res Gagneur J Jackson D B Casari G. Hierarchical analysis of dependency in metabolic networks J. Bioinformatics Lemke N Heredia F Barcellos C K et al. Essentiality and damage in metabolic networks J. Bioinformatics Jeong H Tombor B Albert R et al. The large-scale organization of metabolic networks J. Nature Barabasi A L Oltvai Z N. Network biology understanding the cell's functional organization J. Nat Rev Genet Albert R Jeong H Barabasi A L. Error and attack tolerance of complex networks J. Nature Jeong H Tombor B Albert R et al. The large-scale organization of metabolic networks J. Nature Jeong H Mason S P Barabasi A L et al. Lethality and centrality in protein networks J. Nature Alon U Surette M G Barkai N et al. Robustness in bacterial chemotaxis J. Nature Watts D J Strogatz S H. Collective dynamics of 'small-world'net- 1.. M. works J. Nature Ma H W Zeng A P. Reconstruction of metabolic networks from 2 Knox C Law V Jewison T et al. DrugBank 3. 0 a comprehen- genome data and analysis of their global structure for various organisms J. Bioinformatics J J J Doggrell S A. Migraine and beyond cardio vascular ther apeutic pot ential for CGRP modulators J. Expert Opin Investig Drugs Imada K Leonard W J. The JAK-STAT pathway J. Mol Immunol / Kishimoto T Akira S Narazaki M et al. Interleukin-6 family of cytokines and gp130 J. Blood JAK /STAT TNF-α IL-6 J Funamoto M. Signal transducer and activator of transcription3 is required for glycoprotein130 mediated induction of vascular endo the lial growth factor incardiac myocytes J. Biol Chem

7 Action mechanism of drugs for preventing and treating coronary heart disease based on biological networks ZHANG Yan-ling HUANG Ming-feng QIAO Yan-jiang * Research Center of Traditional Chinese Medicine Information Engineering Beijing University of Chinese Medicine Beijing China Abstract Coronary heart disease CHD related genes and targets as well as drug targets for preventing and treating CHD were taken as the study objects to build a CHD disease network and a drug action network preventing and treating CHD. Such topological characteristic parameters of the networks as degree distribution characteristic path length connectivity and heterogeneity were analyzed to verify the reliability of the networks. On that basis the intersection calculation was conducted for both networks to analyze the drug action mechanism of their sub-networks. The disease network are composed of nodes and sides while the drug action network preventing and treating CHD has nodes and sides. Both of their topological characteristic parameters showed scale-free small world structural characteristics. Two reaction pathways in the sub-networks calcitonin gene-related peptide and IL-6 activated JAK / STAT were taken as examples to discuss the indirect action mechanism for preventing and treating CHD. The results showed that the biological network analysis method combining the disease network and the drug action network is helpful to further studies on the action mechanism of the drugs and significant to the prevention and treatment of diseases. Key words coronary heart disease disease network drug action network network characteristics action mechanism doi /cjcmm http / /tsqb. cintcm. com

maximal clique centralitymcc bottle- neck BN EcCentricity close- ness radiality betweenness stress 11 MATLAB Zscore Zscore metalloproteinase-2 1

maximal clique centralitymcc bottle- neck BN EcCentricity close- ness radiality betweenness stress 11 MATLAB Zscore Zscore metalloproteinase-2 1 * 张燕玲, 黄明峰, 乔延江 ( 北京中医药大学中药信息工程研究室, 北京 100102) 11 - C γ 5. 14-1. 11 81. 6% 79. 2% 81. 5% 78. 3% 79. 6% - 180 42-1 9 coronary heart disease CHD OMIM 390 18 15 28 9 CHD 176 1 2-3 414 Reactome 4 5 Cytoscape2.