Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction

Wiley Interdisciplinary Reviews: Systems Biology and Medicine

Volumn 6, Issue 1, 2014, Pages 77-91

  Ghasemi, Omid ^a,b   Ma, Yonggang ^b,c   Lindsey, Merry L ^b,c,d   Jin, Yu Fang ^a,b  

^a The University of Texas at San Antonio   (United States)

^b San Antonio Cardiovascular Proteomics Center   (United States)

^c UNIVERSITY OF MISSISSIPPI SCHOOL OF MEDICINE   (United States)

^d G V SONNY MONTGOMERY VETERANS AFFAIRS MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALISKIREN; ALISKIREN PLUS VALSARTAN; GELATINASE B; IMMUNOGLOBULIN ENHANCER BINDING PROTEIN; INTERLEUKIN 18; MATRILYSIN; MITOCHONDRIAL PROTEIN; OSTEOPONTIN; TISSUE INHIBITOR OF METALLOPROTEINASE 1; TRANSCRIPTION FACTOR SP1; TRANSFORMING GROWTH FACTOR BETA1; VALSARTAN;

ANGIOGENESIS; BIOLOGICAL FUNCTIONS; CARDITIS; CELL FUNCTION; CLASSIFICATION; COLLAGEN DEGRADATION; DATA ANALYSIS; EXTRACELLULAR MATRIX; EXTRACELLULAR MATRIX REMODELING; GENE EXPRESSION; GENOMICS; HEALING; HEART ELECTROPHYSIOLOGY; HEART INFARCTION; HEART MUSCLE; HUMAN; INFLAMMATION; INFORMATION PROCESSING; MACHINE LEARNING; MATHEMATICAL MODEL; MATRIX ASSISTED LASER DESORPTION IONIZATION TIME OF FLIGHT MASS SPECTROMETRY; NONHUMAN; POLYACRYLAMIDE GEL ELECTROPHORESIS; PROTEIN EXPRESSION; PROTEIN MICROARRAY; PROTEIN PROCESSING; PROTEIN PROTEIN INTERACTION; PROTEOMICS; REGULATORY MECHANISM; REVIEW; SECRETION RATE; SEQUENCE ANALYSIS; SIGNAL PROCESSING; SYSTEMS BIOLOGY; TISSUE REPAIR; TRANSCRIPTOMICS;

ANIMALS; EXTRACELLULAR MATRIX; HUMANS; INFLAMMATION; MYOCARDIAL INFARCTION; SYSTEMS BIOLOGY;

EID: 84890318019     PISSN: 19395094     EISSN: 1939005X     Source Type: Journal    
DOI: 10.1002/wsbm.1248     Document Type: Review

References (110)

1. Matsui Y, Morimoto J, Uede T. Role of matricellular proteins in cardiac tissue remodeling after myocardial infarction. World J Biol Chem 2010, 1:69-80.
2. Birdsall HH, Green DM, Trial J, Youker KA, Burns AR, MacKay CR, LaRosa GJ, Hawkins HK, Smith CW, Michael LH, et al. Complement C5a, TGF-β1, and MCP-1, in sequence, induce migration of monocytes into ischemic canine myocardium within the first one to five hours after reperfusion. Circulation 1997, 95:684-692.
3. Ma Y, Yabluchanskiy A, Lindsey ML. Neutrophil roles in left ventricular remodeling following myocardial infarction. Fibrogenesis Tissue Repair 2013, 6:11.
4. Troidl C, Möllmann H, Nef H, Masseli F, Voss S, Szardien S, Willmer M, Rolf A, Rixe J, Troidl K, et al. Classically and alternatively activated macrophages contribute to tissue remodelling after myocardial infarction. J Cell Mol Med 2009, 13:3485-3496.
5. Ma Y, Halade GV, Zhang J, Ramirez TA, Levin D, Voorhees A, Jin YF, Han HC, Manicone AM, Lindsey ML. Matrix metalloproteinase-28 deletion exacerbates cardiac dysfunction and rupture after myocardial infarction in mice by inhibiting M2 macrophage activation. Circ Res 2013, 112:675-688.
6. Squires CE, Escobar GP, Payne JF, Leonardi RA, Goshorn DK, Sheats NJ, Mains IM, Mingoia JT, Flack EC, Lindsey ML. Altered fibroblast function following myocardial infarction. J Mol Cell Cardiol 2005, 39:699-707.
7. Dewald O, Ren G, Duerr GD, Zoerlein M, Klemm C, Gersch C, Tincey S, Michael LH, Entman ML, Frangogiannis NG. Of mice and dogs: species-specific differences in the inflammatory response following myocardial infarction. Am J Pathol 2004, 164:665-677.
8. Frangogiannis NG. Regulation of the inflammatory response in cardiac repair. Circ Res 2012, 110:159-173.
9. Matsui Y, Ikesue M, Danzaki K, Morimoto J, Sato M, Tanaka S, Kojima T, Tsutsui H, Uede T. Syndecan-4 prevents cardiac rupture and dysfunction after myocardial infarction. Circ Res 2011, 108:1328-1339.
10. Ma Y, Halade GV, Lindsey ML. Extracellular matrix and fibroblast communication following myocardial infarction. J Cardiovasc Transl Res 2012, 5:848-857.
11. Benjamin IJ, Schneider MD. Learning from failure: congestive heart failure in the postgenomic age. J Clin Invest 2005, 115:495-499.
12. Albeck JG, MacBeath G, White FM, Sorger PK, Lauffenburger DA, Gaudet S. Collecting and organizing systematic sets of protein data. Nat Rev Mol Cell Biol 2006, 7:803-812.
13. Kitano H. Computational systems biology. Nature 2002, 420:206-210.
14. Bruggeman FJ, Westerhoff HV. The nature of systems biology. Trends Microbiol 2007, 15:45-50.
15. Adams KF. Systems biology and heart failure: concepts, methods, and potential research applications. Heart Fail Rev 2010, 15:371-398.
16. Chipman A. All systems go. Nature 2006, 439:136-137.
17. Wandelt S, Rheinlander A, Bux M, Thalheim L, Haldemann B, Leser U. Data management challenges in next generation sequencing. Databank Spektrum 2012, 12:161-171.
18. Thiele H, Glandorf J, Hufnagel P, Korting G, Bluggel M. Managing proteomics data: from generation and data warehouse to central data repository. J Proteomics Bioinform 2008, 1:485-507.
19. Swedlow JR, Lewis SE, Goldberg IG. Modelling data across labs, genomes, space and time. Nat Cell Biol 2006, 8:1190-1194.
20. Patterson NL, Iyer RP, de Castro Brás LE, Li Y, Andrews TG, Aune GJ, Lange RA, Lindsey ML. Using proteomics to uncover extracellular matrix interactions during cardiac remodeling. Proteomics Clin Appl 2013, 7:516-527.
21. Barallobre-Barreiro J, Didangelos A, Schoendube FA, Drozdov I, Yin X, Fernández-Caggiano M, Willeit P, Puntmann VO, Aldama-López G, Shah AM, et al. Proteomics analysis of cardiac extracellular matrix remodeling in a porcine model of ischemia/reperfusion injury. Circulation 2012, 125:789-802.
22. de Castro Brás LE, Ramirez TA, DeLeon-Pennell KY, Chiao YA, Ma Y, Dai Q, Halade GV, Hakala K, Weintraub ST, Lindsey ML. Texas 3-step decellularization protocol: looking at the cardiac extracellular matrix. J Proteomics 2013, 86:43-52.
23. Butler GS, Overall CM. Updated biological roles for matrix metalloproteinases and new "intracellular" substrates revealed by degradomics. Biochemistry 2009, 48:10830-10845.
24. Iyer RP, Patterson NL, Fields GB, Lindsey ML. The history of matrix metalloproteinases: milestones, myths, and misperceptions. Am J Physiol 2012, 303:H919-H930.
25. Chang J-H, Javier JAD, Chang G-Y, Oliveira HB, Azar DT. Functional characterization of neostatins, the MMP-derived, enzymatic cleavage products of type XVIII collagen. FEBS Lett 2005, 579:3601-3606.
26. Davis GE, Bayless KJ, Davis MJ, Meininger GA. Regulation of tissue injury responses by the exposure of matricryptic sites within extracellular matrix molecules. Am J Pathol 2000, 156:1489-1498.
27. Ricard-Blum S, Ballut L. Matricryptins derived from collagens and proteoglycans. Front Biosci 2011, 16:674-697.
28. Fichter M, Korner U, Schomburg J, Jennings L, Cole AA, Mollenhauer J. Collagen degradation products modulate matrix metalloproteinase expression in cultured articular chondrocytes. J Orthop Res 2006, 24:63-70.
29. Hamano Y, Zeisberg M, Sugimoto H, Lively JC, Maeshima Y, Yang C, Hynes RO, Werb Z, Sudhakar A, Kalluri R. Physiological levels of tumstatin, a fragment of collagen IV α3 chain, are generated by MMP-9 proteolysis and suppress angiogenesis via αV β3 integrin. Cancer Cell 2003, 3:589-601.
30. Chang C, Werb Z. The many faces of metalloproteases: cell growth, invasion, angiogenesis and metastasis. Trends Cell Biol 2001, 11:S37-S43.
31. Adair-Kirk TL, Senior RM. Fragments of extracellular matrix as mediators of inflammation. Int J Biochem Cell Biol 2008, 40:1101-1110.
32. Chiao YA, Zamilpa R, Lopez EF, Dai Q, Escobar GP, Hakala K, Weintraub ST, Lindsey ML. In vivo matrix metalloproteinase-7 substrates identified in the left ventricle post-myocardial infarction using proteomics. J Proteome Res 2010, 9:2649-2657.
33. Zamilpa R, Lopez EF, Chiao YA, Dai Q, Escobar GP, Hakala K, Weintraub ST, Lindsey ML. Proteomic analysis identifies in vivo candidate matrix metalloproteinase-9 substrates in the left ventricle post-myocardial infarction. Proteomics 2010, 10:2214-2223.
34. Martens L, Hermjakob H, Jones P, Adamski M, Taylor C, States D, Gevaert K, Vandekerckhove J, Apweiler R. PRIDE: The proteomics identifications database. Proteomics 2005, 5:3537-3545.
35. Jones P, Côté RG, Martens L, Quinn AF, Taylor CF, Derache W, Hermjakob H, Apweiler R. PRIDE: a public repository of protein and peptide identifications for the proteomics community. Nucleic Acids Res 2006, 34:D659-D663.
36. Beavis R. Using the global proteome machine for protein identification. Methods Molecular Biology. Canada; 2006, 328:217-228.
37. Xenarios I, Rice DW, Salwinski L, Baron MK, Marcotte EM, Eisenberg D. DIP: the database of interacting proteins. Nucleic Acids Res 2000, 28:289-291.
38. Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Stümpflen V, Mewes HW, et al. The MIPS mammalian protein-protein interaction database. Bioinformatics 2005, 21:832-834.
39. Prasad TS, Kandasamy K, Pandey A. Human protein reference database and human proteinpedia as discovery tools for systems biology. Methods Mol Biol 2009, 577:67-79.
40. Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH, Bähler J, Wood V, et al. The BioGRID interaction database: 2008 update. Nucleic Acids Res 2008, 36:D637-D640.
41. Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res 2012, 40:D841-D846.
42. Persico M, Ceol A, Gavrila C, Hoffmann R, Florio A, Cesareni G. HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms. BMC Bioinformatics 2005, 6(Suppl 4):S21.
43. Hellerstein JM. Quantitative data cleaning for large databases, 2008. Available at: http://db.cs.berkeley.edu/jmh/papers/cleaning-unece.pdf. (Accessed July 15, 2013).
44. Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET. Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 2003, 100:10393-10398.
45. Ghasemi O, Nguyen N, Ramirez TA, Zhang J, Lindsey ML, Jin Y. A Biclustering Approach to Analyse Drug Effects on Extracellular Matrix Remodelling Post-Myocardial Infarction. 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), October 4th-7th, 2012, 143-150, Philadelphia, PA, USA, doi: 10.1109/BIBMW.2012.6470296
46. Stanton LW, Garrard LJ, Damm D, Garrick BL, Lam A, Kapoun AM, Zheng Q, Protter AA, Schreiner GF, White RT. Altered patterns of gene expression in response to myocardial infarction. Circ Res 2000, 86:939-945.
47. Ringner M. What is principal component analysis? Nat Biotechnol 2008, 26:303-304.
48. Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995, 20:273-297.
49. Dhawan A, Wenzel B, George S, Gussak I, Bojovic B, Panescu D, eds. Detection of acute myocardial infarction from serial ECG using multilayer support vector machine. In: Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. 2012 August 28, 2012-September 1, 2012, San Diego, CA.
50. Asl BM, Setarehdan SK, Mohebbi M. Support vector machine-based arrhythmia classification using reduced features of heart rate variability signal. Artif Intell Med 2008, 44:51-64.
51. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, Magri E, Pedriali M, Fabbri M, Campiglio M, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005, 65:7065-7070.
52. Hua S, Sun Z. A novel method of protein secondary structure prediction with high segment overlap measure: support vector machine approach. J Mol Biol 2001, 308:397-407.
53. Penfold CA, Wild DL. How to infer gene networks from expression profiles, revisited. Interface Focus 2011, 1:857-870.
54. Jin Y, Lindsey ML. Stability analysis of genetic regulatory network with additive noises. BMC Genomics 2008, 9(Suppl 1):S21.
55. Stelling J, Gilles ED. Mathematical modeling of complex regulatory networks. IEEE Trans Nanobioscience 2004, 3:172-179.
56. Yeang CH, Ideker T, Jaakkola T. Physical network models. J Comput Biol 2004, 11:243-262.
57. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determination of genetic network architecture. Nat Genet 1999, 22:281-285.
58. Gardner TS, di Bernardo D, Lorenz D, Collins JJ. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 2003, 301:102-105.
59. Chen T, He HL, Church GM. Modeling gene expression with differential equations. Pacific Symposium on Biocomputing. 1999, 29-40.
60. Huang Y, Liu D, Wu H. Hierarchical Bayesian methods for estimation of parameters in a longitudinal HIV dynamic system. Biometrics 2006, 62:413-423.
61. Ghasemi O, Lindsey M, Yang T, Nguyen N, Huang Y, Jin Y-F. Bayesian parameter estimation for nonlinear modelling of biological pathways. BMC Syst Biol 2011, 5:S9, doi: 10.1186/1752-0509-5-S3-S9.
62. Yeung MKS, Tegnér J, Collins JJ. Reverse engineering gene networks using singular value decomposition and robust regression. Proc Natl Acad Sci U S A 2002, 99:6163-6168.
63. van Someren EP, Wessels LF, Reinders MJ. Linear modeling of genetic networks from experimental data. Proc Int Conf Intell Syst Mol Biol 2000, 8:355-366.
64. Escobar M, West M. Bayesian density estimation and inference using mixtures. J Am Stat Assoc 1995, 90:577-588.
65. Verstraete F, Doherty AC, Mabuchi H. Sensitivity optimization in quantum parameter estimation. Phys Rev A 2001, 64:032111.
66. Jin Y, Qu Z, eds. Synchronization of Lorenz systems by adaptive observation. In: American Control Conference, 2003 Proceedings of the 2003. Denver, CO; 2003.
67. Tang Y, Ghosal S, Roy A. Nonparametric Bayesian estimation of positive false discovery rates. Biometrics 2007, 63:1126-1134.
68. Qu Z. Adaptive and robust controls of uncertain systems with nonlinear parameterization. IEEE Trans Automatic Control 2003, 48:1817-1824.
69. Saltelli A, Chan K, Scott M. Sensitivity Analysis. New York: John Wiley & Sons; 2000.
70. Khalil HK. Nonlinear Systems. 3rd ed. Upper Saddle River, NJ: Prentice Hall; 2001.
71. Samoilov M, Plyasunov S, Arkin AP. Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations. Proc Natl Acad Sci U S A 2005, 102:2310-2315.
72. Manninen T, Linne M-L, Ruohonen K. Developing Itô stochastic differential equation models for neuronal signal transduction pathways. Comput Biol Chem 2006, 30:280-291.
73. Arkin A, Ross J, McAdams HH. Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 1998, 149:1633-1648.
74. Jin Y, Han HC, Berger J, Dai Q, Lindsey ML. Combining experimental and mathematical modeling to reveal mechanisms of macrophage-dependent left ventricle remodeling. BMC Syst Biol 2011, 5:60.
75. Chow CC, Clermont G, Kumar R, Lagoa C, Tawadrous Z, Gallo D, Betten B, Bartels J, Constantine G, Fink MP, et al. The acute inflammatory response in diverse shock states. Shock 2005, 24:74-84.
76. Karagiannis ED, Popel AS. A theoretical model of type I collagen proteolysis by matrix metalloproteinase (MMP) 2 and membrane type 1 MMP in the presence of tissue inhibitor of metalloproteinase 2. J Biol Chem 2004, 279:39105-39114.
77. Vempati P, Karagiannis ED, Popel AS. A biochemical model of matrix metalloproteinase 9 activation and inhibition. J Biol Chem 2007, 282:37585-37596.
78. Noble D. Modelling the heart: insights, failures and progress. Bioessays 2002, 24:1155-1163.
79. Cassman MC, Arkin A, Doyle F, Katagiri F, Lauffenburger D, Stokes C. International Research and Development in Systems Biology. Dordrecht, The Netherlands: Springer; 2005.
80. Winslow RL, Scollan DF, Holmes A, Yung CK, Zhang J, Jafri MS. Electrophysiological modeling of cardiac ventricular function: from cell to organ. Annu Rev Biomed Eng 2000, 2:119-155.
81. Luo C, Rudy Y. A dynamic model of the cardiac ventricular action potential. II. After depolarizations, triggered activity, and potentiation. Circ Res 1994, 74:1097-1113.
82. DiFrancesco D, Noble D. A model of cardiac electrical activity incorporating ionic pumps and concentration changes. Philos Trans R Soc Lond B Biol Sci 1985, 307:353-398.
83. 2+ dynamics: the roles of ryanodine receptor adaptation and sarcoplasmic reticulum load. Biophys J 1998, 74:1149-1168.
84. Freeman LC. Centrality in social networks conceptual clarification. Soc Netw 1979, 1:215-239.
85. Junker BH, Schreiber F. Analysis of Biological Networks. Hoboken, NJ: Wiley-Interscience; 2008.
86. Harary F. Graph Theory. Reading, MA: Addison-Wesley Pub. Co.; 1969.
87. Wasserman S, Faust K. Social Network Analysis: Methods and Applications. Cambridge, New York: Cambridge University Press; 1994.
88. Raman K. Construction and analysis of protein-protein interaction networks. Autom Exp 2010, 2:2.
89. Chen L, Xuan J, Riggins RB, Wang Y, Clarke R. Identifying protein interaction subnetworks by a bagging Markov random field-based method. Nucleic Acids Res 2013, 41:e42.
90. Jahid MJ, Ruan J. A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis. BMC Genomics 2012, 13:S8.
91. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009, 4:44-57.
92. Huang DW, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2009, 37:1-13.
93. Chi A, Valencia JC, Hu ZZ, Watabe H, Yamaguchi H, Mangini NJ, Huang H, Canfield VA, Cheng KC, Yang F, et al. Proteomic and bioinformatic characterization of the biogenesis and function of melanosomes. J Proteome Res 2006, 5:3135-3144.
94. Hu ZZ, Valencia JC, Huang H, Chi A, Shabanowitz J, Hearing VJ, Appella E, Wu C. Comparative bioinformatics analyses and profiling of lysosome-related organelle proteomes. Int J Mass Spectrom 2007, 259:147-160.
95. Hu ZZ, Kagan BL, Ariazi EA, Rosenthal DS, Zhang L, Li JV, Huang H, Wu C, Jordan VC, Riegel AT, et al. Proteomic analysis of pathways involved in estrogen-induced growth and apoptosis of breast cancer cells. PLoS One 2011, 6:e20410.
96. Hu ZZ, Huang H, Cheema A, Jung M, Dritschilo A, Wu CH. Integrated bioinformatics for radiation-induced pathway analysis from proteomics and microarray data. J Proteomics Bioinform 2008, 1:47-60.
97. Nguyen N, Zhang X, Wang Y, Han HC, Jin Y, Schmidt G, Lange RA, Chilton RJ, Lindsey ML. Targeting myocardial infarction-specific protein interaction network using computational analyses. 2011 IEEE International Workshop on Genomic Signal Processing and Statistics, San Antonio, TX; 2011.
98. Azuaje FJ, Rodius S, Zhang L, Devaux Y, Wagner DR. Information encoded in a network of inflammation proteins predicts clinical outcome after myocardial infarction. BMC Med Genomics 2011, 4:59.
99. Ren G, Liu Z. NetCAD: a network analysis tool for coronary artery disease-associated PPI network. Bioinformatics 2013, 29:279-280.
100. Wu LH, Wang Y, Nie J, Fan XH, Cheng YY. A network pharmacology approach to evaluating the efficacy of Chinese medicine using genome-wide transcriptional expression data. Evid Based Complement Alternat Med 2013, 2013:915343.
101. Zhu WL, Yang L, Shan HL, Zhang Y, Zhou R, Su Z, et al. MicroRNA expression analysis: clinical advantage of propranolol reveals key microRNAs in myocardial infarction. PLoS One 2011, 6:e14736.
102. Daskalopoulou SS, Delaney JAC, Filion KB, Brophy JM, Mayo NE, Suissa S. Discontinuation of statin therapy following an acute myocardial infarction: a population-based study. Eur Heart J 2008, 29:2083-2091.
103. Bonab EM, Ghasemi O, Jamshidi M, Jin Y. Time Delay Estimation in Gene Regulatory Networks. Proc. of the 2013 8th International Conference on System of Systems Engineering, June 2nd-6th, 2013, 302-307, Maui, HI, USA, doi: 10.1109/SYSoSE.2013.6575284
104. Berhane BT, Zong C, Liem DA, Huang A, Le S, Edmondson RD, Jones RC, Qiao X, Whitelegge JP, Ping P, et al. Cardiovascular-related proteins identified in human plasma by the HUPO Plasma Proteome Project pilot phase. Proteomics 2005, 5:3520-3530.
105. Rothemund DL, Locke VL, Liew A, Thomas TM, Wasinger V, Rylatt DB. Depletion of the highly abundant protein albumin from human plasma using the Gradiflow. Proteomics 2003, 3:279-287.
106. Tam SW, Pirro J, Hinerfeld D. Depletion and fractionation technologies in plasma proteomic analysis. Expert Rev Proteomics 2004, 1:411-420.
107. Chen R, Snyder M. Systems biology: personalized medicine for the future? Curr Opin Pharmacol 2012, 12:623-628.
108. Mayr M. From data gathering to systems medicine. Cardiovasc Res 2013, 97:599-600.
109. Loscalzo J. Personalized cardiovascular medicine and drug development: time for a new paradigm. Circulation 2012, 125:638-645.
110. Roukos HD. Biotechnological, genomics and systems-synthetic biology revolution: redesigning genetic code for a pragmatic systems medicine. Expert Rev Med Devices 2012, 9:97-101.