Journal of Molecular and Cellular Cardiology
Volume 44, Issue 3 , Pages 460-469 , March 2008

An overview of cardiac systems biology

Received 16 October 2007 ,Revised 7 December 2007 ,Accepted 13 December 2007.

References 

  1. Ng A, Bursteinas B, Gao Q, Mollison E, Zvelebil M. Resources for integrative systems biology: from data through databases to networks and dynamic system models. Brief Bioinform. 2006;7:318–330
  2. Kitano H. Systems biology: a brief overview. Science. 2002;295:1662–1664
  3. Kitatno H. Computational systems biology. Nature. 2002;420:206–210
  4. Barabasi AL, Bonabeau E. Scale-free networks. Sci Amer. 2003;288:60–69
  5. Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004;5:101–113
  6. Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999;286:509–512
  7. Booch G. Object Oriented Analysis and Design. 2nd ed.. Calif: Benjamin/Cummings; 1994;
  8. Liew CC, Dzau VJ. Molecular genetics and genomics of heart failure. Nat Rev Genet. 2004;5:811–825
  9. Barrans JD, Liew CC. Chipping away at heart failure. Methods Mol Med. 2006;126:157–169
  10. Donahue MP, Marchuk DA, Rockman HA. Redefining heart failure: the utility of genomics. J Amer Coll Cardiol. 2006;48:1289–1298
  11. Winslow RL, Boguski MS. Genome informatics: current status and future prospects. Circ Res. 2003;92:953–961
  12. McGregor E, Dunn MJ. Proteomics of the heart: unraveling disease. Circ Res. 2006;98:309–321
  13. Van Eyk JE. Proteomics: unraveling the complexity of heart disease and striving to change cardiology. Curr Opin Mol Therapeu. 2001;3:546–553
  14. Scobioala S, Klocke R, Michel G, Kuhlmann M, Nikol S. Proteomics: state of the art and its application in cardiovascular research. Curr Med Chem. 2004;11:3203–3218
  15. Arrell DK, Neverova I, Van Eyk JE. Cardiovascular proteomics: evolution and potential. Circ Res. 2001;88:763–773
  16. Vasan RS. Biomarkers of cardiovascular disease: molecular basis and practical considerations. Circulation. 2006;113:2335–2362
  17. White MY, Van Eyk JE. Cardiovascular proteomics: past, present, and future. Mol Diagn Ther. 2007;11:83–95
  18. Matt P, Carrel T, White M, Lefkovits I, Van Eyk J. Proteomics in cardiovascular surgery. J Thorc Cardiovasc Surg. 2007;133:210–214
  19. Griffin J. Metabolic profiles to define the genome: can we hear the phenotypes?. Philos Trans R Soc Lond B Biol Sci. 2004;359:857–871
  20. David GJ. Metabolite profiling and cardiovascular disease. In:  Lindon J,  Nicholson J,  Holmes E editor. The Hand book of Metabonomics and Metabolomics. Elsevier; 2006;
  21. Grainger DJ. Metabolic profiling in heart disease. Heart Metab [serial online]. 2006;32:22–25Available from: URL:http://www.heartandmetabolism.org/issues/HM32/HM32newtherape.asp
  22. Jenuth JP. The NCBI. Publicly available tools and resources on the Web. Methods Mol Biol. 2000;132:301–312
  23. Edgar R, Barrett T. NCBI GEO standards and services for microarray data. Nat Biotechnol. 2006;24:1471–1472
  24. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al. NCBI GEO: mining tens of millions of expression profiles-database and tools update. Nucleic Acids Res. 2007;35:D760–D765(Database issue)
  25. Park I, Hong SE, Kim TW, Lee J, Oh J, Choi E, et al. Comprehensive identification and characterization of novel cardiac genes in mouse. J Mol Cell Cardiol. 2007;43:93–106
  26. White SM, Constantin PE, Claycomb WC. Cardiac physiology at the cellular level: use of cultured HL-1 cardiomyocytes for studies of cardiac muscle cell structure and function. Am J Physiol Heart Circ Physiol. 2004;286:H823–H829
  27. Mcculloch AD, Paternostro G. Cardiac systems biology. Ann NY Acad Sci. 2005;1047:283–295
  28. Paternostro G, Vignola C, Bartsch D, Omens JH, McCulloch AD, Reed JC. Age-associated cardiac dysfunction in Drosophila melanogaster. Circ Res. 2001;88:1053–1058
  29. Hu N, Sedmera D, Yost HJ, Clark EB. Structure and function of the developing Zebrafish Heart. THE ANATOMICAL RECORD. 2000;260:148–157
  30. Joyce AR, Palsson BO. The model organism as a system: integrating ‘-omics’ data sets. Nat Rev Mol Cell Biol. 2006;7:198–210
  31. Mayr M, Madhu B, Xu Q. Proteomics and metabolomics combined in cardiovascular research. Trends in cardiovasc Med. Feb 2007;17:43–48
  32. Hwang D, Rust AG, Ramsey S, Smith JJ, Leslie DM, Weston AD, et al. A data integration methodology for systems biology. Proc Natl Acad Sci U S A. 2005;102:17296–17301
  33. Hwang D, Smith JJ, Leslie DM, Weston AD, Rust AG, Ramsey S, et al. A data integration methodology for systems biology: experimental verification. Proc Natl Acad Sci U S A. 2005;102:17302–17307
  34. Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL. GenBank. Nucleic Acids Res. 2007;35:D21–D25(Database issue)
  35. Wheeler DL, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2007;35:D5–D12(Database issue)
  36. The Universal Protein Resource (UniProt). Nucleic Acids Res. 2007;35:D193–D197(Database issue)
  37. Couto FM, Silva MJ, Lee V, Dimmer E, Camon E, Apweiler R, et al. GO annotator: linking protein GO annotations to evidence text. J Biomed Discov Collab. 2006;1:1–19
  38. Mulder NJ, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, et al. New developments in the InterPro database. Nucleic Acids Res. 2007;35:D224–D228(Database issue)
  39. Mishra GR, Suresh M, Kumaran K, Kannabiran N, Suresh S, Bala P, et al. Human protein reference database—2006 update. Nucleic Acids Res. 2006;34:D411–D414(Database issue)
  40. Kanehisa M, Goto S, Kawashima S, Okuno Y, Hattori M. The KEGG resource for deciphering the genome. Nucleic Acids Res. 2004;32:D277–D280(Database issue)
  41. Safran M, Solomon I, Shmueli O, Lapidot M, Shen-Orr S, Adato A, et al. GeneCards 2002: towards a complete, object-oriented, human gene compendium. Bioinformatics. 2002;18:1542–1543
  42. Lenhard B, Hayes WS, Wasserman WW. GeneLynx: a gene-centric portal to the human genome. Genome Res. 2001;11:2151–2157
  43. Lenhard B, Wahlestedt C, Wasserman WW. GeneLynx mouse: integrated portal to the mouse genome. Genome Res. 2003;13:1501–1504
  44. Frezal J. Genatlas database, genes and development defects. C R Acad Sci III. 1998;321:805–817
  45. Hubbard TJ, Aken BL, Beal K, Ballester B, Caccamo M, Chen Y, et al. Ensembl 2007. Nucleic Acids Res. 2007;35:D610–D617(Database issue)
  46. Wittig U, Golebiewski M, Kania R, Krebs O, Mir S, Weidemann A, et al. Integration and curation of reaction kinetics data. In: proceedings of the 3rd International workshop on Data Integration in the Life Sciences. 4075:2006;p. 9–103
  47. Cotter D, Guda P, Fahy E, Subramaniam S. MitoProteome: mitochondrial protein sequence database and annotation system. Nucleic Acids Res. 2004;32:D463–D467(Database issue)
  48. Zhang Q, Lu M, Shi L, Rui W, Zhu X, Chen G, et al. Cardio: a web-based knowledge resource of genes and proteins related to cardiovascular disease. Int J Cardiol. 2004;97:245–249
  49. Hong SE, Rho SH, Yeom YI, Kim DH. HCNet: a database of heart and calcium functional network. Bioinformatics. 2006;22:2053–2054
  50. CIDMS:Cardiac Integrated Database Managment System, version 1.0. [Online] Available from: URL: http://cidms.org
  51. Akman V, Surav M. Steps toward formalizing context. AI Magazine. 1996;17:
  52. Burek P, Hoehndorf R, Loebe F, Visagie J, Herre H, Kelso J. A top-level ontology of functions and its application in the Open Biomedical Ontologies. Bioinformatics. 2006;22:66–73
  53. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25:25–29
  54. Rosse C, Mejino JL. A reference ontology for biomedical informatics: the foundational model of anatomy. J Biomed Inform. 2003;36:478–500
  55. Berner-Lee T, Hendler J. Publishing on the semantic web. Nature. 2001;1023–1024
  56. Summary Report: W3C Workshop on Semantic Web for Life Sciences. [Online]. Nov 22 2004;Available from: URL:http//www.w3.org/2004/10/swls-workshop-report.html
  57. In:  Baker CJO,  Cheung H editor. Semantic Web: revolutionizing knowledge discovery in the life sciences. New York: Springer; 2007;
  58. Wang X, Gorlitsky R, Almeida JS. From XML to RDF: how semantic web technologies will change the design of ‘-omic’ standards. Nat Biotechnol. 2005;23:1099–1103
  59. Bratsas CH, Quaresma P, Pangalos G, Maglaveras N. Using ontologies to build a knowledge base of cardiology problems and algorithms. In: Proceedings 31st annual meeting of CinC; Chicago, USA. 2004;
  60. Gedzelman S, Simonet M, Bernhard D, Diallo G, Palmer P. Building an ontology of cardio-vascular diseases for concept-based information retrieval. In: Proceedings 32nd annual meeting of CinC; Lyon, France. 2005;
  61. Stromback L, Hall D, Lambrix P. A review of standards for data exchange within systems biology. Proteomics. 2007;7:857–867
  62. Klipp E, Liebermeister W, Helbig A, Kowald A, Schaber J. Systems biology standards—the community speaks. Nat Biotechnol. 2007;25:390–391
  63. Brazma A, Krestyaninova M, Sarkans U. Standards for systems biology. Nat Rev Genet. 2006;7:593–605
  64. Bard JB, Rhee SY. Ontologies in biology: design, applications and future challenges. Nat Rev Genet. 2004;5:213–222
  65. Soldatova LN, King RD. Are the current ontologies in biology good ontologies?. Nat Biotechnol. 2005;23:1095–1098
  66. Quackenbush J. Data standards for ‘-omic’ science. Nat Biotechnol. 2004;22:613–614
  67. Bhalla US, Iyengar R. Emergent properties of networks of biological signaling pathways. Science. 1999;283:381–387
  68. Davidenko JM, Pertsov AV, Salomonsz R, Baxter W, Jalife J. Stationary and drifting spiral waves of excitation in isolated cardiac muscle. Nature. 1992;355:349–351
  69. Kitano H, Funahashi A, Matsuoka Y, Jouraku A. Using process diagrams for the graphical representation of biological networks. Nat Biotechnol. 2005;23:961–966
  70. Funahashi A, Matsuoka Y, Jouraku A, Kitano H. Celldesigner: a modeling tool for biochemical networks. In: Proceedings of the 2006 Winter Simulation Conference. 2006;p. 1707–1712
  71. Suderman M, Hallett Michael. Tools for visually exploring biological networks. Bioinformatics. 2007;23:2651–2659
  72. Zhu X, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev. 2007;21:1010–1024
  73. Papin JA, Reed JL, Palsson BO. Hierarchical thinking in network biology: the unbiased modularization of biochemical networks. Trends Biochem Sci. 2004;2912:641–647
  74. Lee NH. Genomic approaches for reconstructing gene networks. Pharmacogenomics. 2005;6:245–258
  75. Piano F, Kristin GC, Hill DE, Vidal MC. Elegans network biology: a beginning. WormBookorg [serial online]. 2006;Availabe from: URL: http://www.wormbook.org/chapters/www_networkbio/networkbio.html
  76. Xia Y, Yu H, Jansen R, Seringhaus M, Baxter S, Greenbaum D, et al. Analyzing cellular biochemistry in terms of molecular networks. Annu Rev Biochem. 2004;73:1051–1087
  77. Albert R. Scale-free networks in cell biology. J Cell Sci. 2005;118:4947–4957
  78. Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D. How to infer gene networks from expression profiles. Mol Syst Biol. 2007;3:78–88
  79. Weiss JN, Yang L, Qu Z. Systems biology approaches to metabolic and cardiovascular disorders: network perspectives of cardiovascular metabolism. J Lipid Res. 2006;47:2355–2366
  80. Drake TA, Schadt EE, Lusis AJ. Integrating genetic and gene-expression data: application to cardiovascular and metabolic traits in mice. Mamm Genome. 2006;17:466–479
  81. Ashley EA, Ferrara R, King JY, Vailaya A, Kuchinsky A, He X, et al. Network analysis of human in-stent restenosis. Circulation. 2006;114:2644–2654
  82. Ghazalpour A, Doss S, Yang X, Aten J, Toomey EM, Van Nas A, et al. Thematic review series: the pathogenesis of atherosclerosis. Toward a biological network for atherosclerosis. J Lipid Res. 2004;45:1793–1805
  83. Kitano H. Biological robustness. Nat Rev Genet. 2004;5:826–837
  84. Ge H, Walhout AJ, Vidal M. Integrating ‘-omic’ information: a bridge between genomics and systems biology. Trends Genet. 2003;19:551–560
  85. Noble D. Systems biology and the heart. Biosystems. 2006;83:75–80
  86. Ideker T, Lauffenburger D. Building with a scaffold: emerging strategies for high- to low-level cellular modeling. Trends Biotechnol. 2003;21:255–262
  87. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, et al. Towards a proteome-scale map of the human protein–protein interaction network. Nature. 2005;437:1173–1178
  88. Ramani AK, Bunescu RC, Mooney RJ, Marcotte EM. Consolidating the set of known human protein–protein interactions in preparation for large-scale mapping of the human interactome. Genome Biol. 2005;6:R40.0–R40.12
  89. Oda K, Matsuoka Y, Funahashi A, Kitano H. A comprehensive pathway map of epidermal growth factor receptor signaling. Mol Syst Biol. 2005;1:2005.0010
  90. Futschik ME, Chaurasia G, Herzel H. Comparison of human protein–protein interaction maps. Bioinformatics. 2007;23:605–611
  91. Luo RY, Liao S, Tao GY, Li YY, Zeng S, Li YX, et al. Dynamic analysis of optimality in myocardial energy metabolism under normal and ischemic conditions. Mol Syst Biol. 2006;2:2006.0031
  92. Nobel D. Cardiac action and pace maker potentials based on the Hodgkin–Huxley equations. Nature. 1960;188:495–497
  93. Noble D, Rudy Y. Models of cardiac ventricular action potentials: iterative interaction between experiment and simulation. Philos Trans: Mats, Phys and Engi Sci. 2001;359:1127–1142
  94. DiFrancesco D, Nobel D. A model of cardiac electical activity incorporating ionic pumps and concentration changes. Philos Trans R Soc B Biol Sci. 1985;307:353–398
  95. McCulloch AD. Functionally and structurally integrated computational modeling of ventricular physiology. Jpn J Physiol. 2004;54:531–539
  96. Nobel D. The future: putting Humpty-Dumpty together again. Biochem Soc Trans. 2003;31:156–158
  97. Smith NP, Crampin EJ, Niederer SA, Bassingthwaighte JB, Beard DA. Computational biology of cardiac myocytes: proposed standards for the physiome. J Exp Biol. 2007;210:1576–1583
  98. Hunter P, Nielsen P. A strategy for integrative computational physiology. Physiology (Bethesda, Md). 2005;20:316–325
  99. Hunter P, Smith N, Fernandez J, Tawhai M. Integration from proteins to organs: the IUPS Physiome Project. Mech Ageing Dev. 2005;126:187–192
  100. Winslow RL, Greenstein JL. The ongoing journey to understand heart function through integrative modeling. Circ Res. 2004;95:1135–1136
  101. Luo CH, Rudy Y. A dynamic model of the cardiac ventricular action potential. I. Simulations of ionic currents and concentration changes. Circ Res. 1994;74:1071–1096
  102. Jafri MS, Rice JJ, Winslow RL. Cardiac Ca2+ dynamics: the roles of ryanodine receptor adaptation and sarcoplasmic reticulum load. Biophys J. 1998;74:1149–1168
  103. Mazhari R, Greenstein JL, Winslow RL, Marban E, Nuss HB. Molecular interactions between two long-QT syndrome gene products, HERG and KCNE2, rationalized by in vitro and in silico analysis. Circ Res. 2001;89:33–38
  104. Irvine LA, Jafri MS, Winslow RL. Cardiac sodium channel Markov model with temperature dependence and recovery from inactivation. Biophys J. 1999;76:1868–1885
  105. Rice JJ, Jafri MS, Winslow RL. Modeling short-term interval-force relations in cardiac muscle. Am J physiol Heart Circ Physiol. 2000;278:H913–H931
  106. Rice JJ, Jafri MS, Winslow RL. Modelling short-term interval-force relations in cardiac muscle. Ann NY Acad Sci. 1998;853:345–349
  107. Rice JJ, Jeremy J, Jafri MS, Winslow RL. Computational modeling of short-term interval-force relations in single cardiac myocytes. Ann Biomed Eng. 2000;28(SUPPL.1):S–89
  108. Deserranno D, Kassemi M, Thomas J. Incorporation of myofilament activation mechanics into a lumped model of the human heart. Ann Biomed Eng. 2007;35:321–336
  109. Niederer SA, Hunter PJ, Smith NP. A quantitative analysis of cardiac myocyte relaxation: a simulation study. Biophys J. 2006;90:1697–1722
  110. Winslow RL, Tanskanen A, Chen M, Greenstein JL. Multi-scale modeling of calcium signaling in the cardiac dyad. Ann NY Acad Sci. 2006;1080:362–375
  111. Hinch R, Greenstein JL, Winslow RL. Multi-scale models of local control of calcium induced calcium release. Prog Biophys Mol Biol. 2006;90:136–150
  112. Winslow RL, Greenstein JL. Biophysical models of the cardiovascular system. In:  Stephanopoulos IRaG editors. Systems Biology. Networks, Models, and Applications. vol. II:Oxford: University Press; 2007;p. 265–296
  113. Cortassa S, Aon MA, Marban E, Winslow RL, O'Rourke B. An integrated model of cardiac mitochondrial energy metabolism and calcium dynamics. Biophys J. 2003;84:2734–2755
  114. Vo TD, Palsson BO. Building the power house: recent advances in mitochondrial studies through proteomics and systems biology. Am J Physiol Cell Physiol. 2007;292:C164–C177
  115. Saucerman JJ, McCulloch AD. Mechanistic systems models of cell signaling networks: a case study of myocyte adrenergic regulation. Prog Biophys Mol Biol. 2004;85:261–278
  116. Saucerman JJ, Brunton LL, Michailova AP, McCulloch AD. Modeling beta-adrenergic control of cardiac myocyte contractility in silico. J Biol Chem. 2003;278:47997–48003
  117. Michailova A, Saucerman J, Belik ME, McCulloch AD. Modeling regulation of cardiac KATP and L-type Ca2+ currents by ATP, ADP, and Mg2+. Biophys J. 2005;88:2234–2249
  118. Saucerman JJ, Zhang J, Martin JC, Peng LX, Stenbit AE, Tsien RY, et al. Systems analysis of PKA-mediated phosphorylation gradients in live cardiac myocytes. Proc natl Acad Sci U S A. 2006;103:12923–12928
  119. Cortassa S, Aon M, O'Rourke B, Jacques R, Tseng H-J, Marban E, et al. A computational model integrating electrophysiology, contraction, and mitochondrial bioenergetics in the ventricular myocyte. Biophys J. 2006;91:1564–1589
  120. Winslow RL, Cortassa S, Greenstein JL. Using models of the myocyte for functional interpretation of cardiac proteomic data. J Physiol. 2005;563:73–81
  121. Swertz MA, Jansen RC. Beyond standardization: dynamic software infrastructures for systems biology. Nat Rev Genet. 2007;8:235–243
  122. Shannon PT, Reiss DJ, Bonneau R, Baliga NS. The Gaggle: an open-source software system for integrating bioinformatics software and data sources. BMC bioinformatics. 2006;7:176–189
  123. Hucka M, Finney A, Sauro H, Bolouri H, Doyle J, Kitano H. The ERATO Systems Biology Workbench: enabling interaction and exchange between software tools for computational biology. PacSympBiocomput. 2002;7:450–461
  124. Kumar SP, Feidler JC. BioSPICE: a computational infrastructure for integrative biology. Omics. 2003;7:225–225
  125. Stromback L, Jakoniene V, Tan H, Lambrix P. Representing, storing and accessing molecular interaction data: a review of models and tools. Brief Bioinform. 2006;7:331–338
  126. Puglisi JL, Bers DM. LabHEART: an interactive computer model of rabbit ventricular myocyte ion channels and Ca transport. Am J Physiol Cell Physiol. 2001;281:C2049–C2060
  127. Sarai N, Matsuoka S, Noma A. simBio: a Java package for the development of detailed cell models. Prog Biophys Mol Biol. 2006;90:360–377
  128. Demir SS. Computational modeling of cardiac ventricular action potentials in rat and mouse: review. Jpn J Physiol. 2004;54:523–530

PII: S0022-2828(07)01348-X

doi: 10.1016/j.yjmcc.2007.12.005

Journal of Molecular and Cellular Cardiology
Volume 44, Issue 3 , Pages 460-469 , March 2008