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Computational Tools and Resources for Integrative Modeling in Systems Biology

Abstract

Mathematical modeling is key for systems level understanding of cellular processes. The development of mathematical models demands advanced computational tools that keep track of heterogeneous data of molecules and their interactions. Especially the integration of experimental data and pre-existing knowledge into computational models of biological systems is of considerable importance. In silico simulations of model behavior under similar conditions as in the experiment give the possibility for model validation regarding specific experimental data. Such an integrative approach leads eventually to a more accurate and consistent description of the observed biological system. We review several resources and computational tools which support the investigation of biological networks and describe several resources and methods for integrative modeling.

Keywords

Omics data Mathematical modeling Software tools Network analysis Reverse engineering 

Acronyms/Abbreviations

ARACNE

Algorithm for the Reconstruction of Accurate Cellular Networks

ATP

Adenosine TriPhosphate

BioPAX

Biological Pathway Exchange

BRENDA

BRaunschweig ENzyme Database

CCLE

Cancer Cell Line Excyclopedia

CellML

Cell Markup Language

ChEBI

Chemical Entities of Biological Interest

ChiP

Chromatin immunoprecipitation

COPASI

COmplex PAthway Simulator

DNA

Deoxyribonucleic Acid

DNase

Deoxyribonuclease

DREAM

Dialogue on Reverse Engineering Assessment and Methods

FBA

Flux-Balance Analysis

GEDAS

Gene Expression Data Analysis Suite

GENT

Gene Expression database of Normal and Tumor tissues

GEO

Gene Expression Omnibus

GO

Gene Ontology

GXD

Gene eXpression Database

HGNC

HUGO Gene Nomenclature Committee

HMDB

Human Metabolite DataBase

HUGO

Human Genome Organisation

KEGG

Kyoto Encyclopedia of Genes and Genomes

MCA

Metaboloc Control Analysis

MeDIP

Methylated DNA immunoprecipitation

MMMDB

Mouse Multiple tissue Metabolome DataBase

MOPED

Model Organism Protein Expression Database

mRNA

messenger RNA

MS

Mass Spectrometry

NEST

Neighborhood-based Entity SeT

NMR

Nuclear Magentic Resonace

ODE

Ordinary Differential Equation

PaxDB

Protein Abundance across organisms DataBase

PCA

Principal Component Analysis

RNA

Ribonucleic Acid

SABIO-RK

System for the Analysis of Biochemical Pathways - Reaction Kinetics)

SBGN

Systems Biology Graphical Notation

SBML

Systems Biology Markup Language

SMD

Stanford Microarray Database

SVM

Support Vector Machine

TCGA

The Cancer Genome Atlas

TRED

Transcriptional Regulatory Element Database

VANTED

Visualization and Analysis of Networks containing Experimental Data

XML

eXtensible Markup Language

References

  1. 1.
    Akao T, Yashiro I, Hosoyama A et al (2011) Whole-genome sequencing of sake Yeast Saccharomyces cerevisiae Kyokai no. 7. DNA Res. doi: 10.1093/dnares/dsr029 PubMedCentralPubMedGoogle Scholar
  2. 2.
    Albert R (2005) Scale-free networks in cell biology. J Cell Sci 118:4947–4957. doi: 10.1242/jcs.02714 PubMedGoogle Scholar
  3. 3.
    Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97. doi: 10.1103/RevModPhys.74.47 Google Scholar
  4. 4.
    Assenov Y, Ramírez F, Schelhorn S-E et al (2008) Computing topological parameters of biological networks. Bioinformatics 24:282–284. doi: 10.1093/bioinformatics/btm554 PubMedGoogle Scholar
  5. 5.
    Bader GD, Cary MP, Sander C (2006) Pathguide: a pathway resource list. Nucleic Acids Res 34:D504–D506. doi: 10.1093/nar/gkj126 PubMedCentralPubMedGoogle Scholar
  6. 6.
    Baker M (2012) Quantitative data: learning to share. Nat Methods 9:39–41. doi: 10.1038/nmeth.1815 Google Scholar
  7. 7.
    Balaji S, Iyer LM, Aravind L, Babu MM (2006) Uncovering a hidden distributed architecture behind scale-free transcriptional regulatory networks. J Mol Biol 360:204–212. doi: 10.1016/j.jmb.2006.04.026 PubMedGoogle Scholar
  8. 8.
    Ballester B, Johnson N, Proctor G, Flicek P (2010) Consistent annotation of gene expression arrays. BMC Genomics 11:294. doi: 10.1186/1471-2164-11-294 PubMedCentralPubMedGoogle Scholar
  9. 9.
    Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D (2007) How to infer gene networks from expression profiles. Mol Syst Biol 3:78. doi: 10.1038/msb4100120 PubMedCentralPubMedGoogle Scholar
  10. 10.
    Barabási A-L, Oltvai ZN (2004) Network biology: understanding the cell’s functional organization. Nat Rev Genet 5:101–113. doi: 10.1038/nrg1272 PubMedGoogle Scholar
  11. 11.
    Barretina J, Caponigro G, Stransky N et al (2012) The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607. doi: 10.1038/nature11003 PubMedCentralPubMedGoogle Scholar
  12. 12.
    Bauer T, Eils R, König R (2011) RIP: the regulatory interaction predictor—a machine learning-based approach for predicting target genes of transcription factors. Bioinformatics 27:2239–2247. doi: 10.1093/bioinformatics/btr366 PubMedGoogle Scholar
  13. 13.
    Belcastro V, Siciliano V, Gregoretti F et al (2011) Transcriptional gene network inference from a massive dataset elucidates transcriptome organization and gene function. Nucl Acids Res 39:8677–8688. doi: 10.1093/nar/gkr593 PubMedCentralPubMedGoogle Scholar
  14. 14.
    Bentley DR, Balasubramanian S, Swerdlow HP et al (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456:53–59. doi: 10.1038/nature07517 PubMedCentralPubMedGoogle Scholar
  15. 15.
    Bollard ME, Contel NR, Ebbels TMD et al (2009) NMR-based metabolic profiling identifies biomarkers of liver regeneration following partial hepatectomy in the rat. J Proteome Res 9:59–69. doi: 10.1021/pr900200v Google Scholar
  16. 16.
    Borisuk Tyson (1998) Bifurcation analysis of a model of mitotic control in frog eggs. J Theor Biol 195:69–85. doi: 10.1006/jtbi.1998.0781 PubMedGoogle Scholar
  17. 17.
    Boyer LA, Lee TI, Cole MF, Johnstone SE, Levine SS, Zucker JP, Guenther MG, Kumar RM, Murray HL, Jenner RG, Gifford DK, Melton DA, Jaenisch R & Young RA (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122:947–956Google Scholar
  18. 18.
    Boyle AP, Davis S, Shulha HP et al (2008) High-resolution mapping and characterization of open chromatin across the genome. Cell 132:311–322. doi: 10.1016/j.cell.2007.12.014 PubMedCentralPubMedGoogle Scholar
  19. 19.
    Brown KR, Otasek D, Ali M et al (2009) NAViGaTOR: network analysis, visualization and graphing Toronto. Bioinformatics 25:3327–3329. doi: 10.1093/bioinformatics/btp595 PubMedCentralPubMedGoogle Scholar
  20. 20.
    Camacho D, Licona PV, Mendes P, Laubenbacher R (2007) Comparison of reverse-engineering methods using an in silico network. Ann N Y Acad Sci 1115:73–89. doi: 10.1196/annals.1407.006 PubMedGoogle Scholar
  21. 21.
    Di Camillo B, Toffolo G, Cobelli C (2009) A gene network simulator to assess reverse engineering algorithms. Ann N Y Acad Sci 1158:125–142. doi: 10.1111/j.1749-6632.2008.03756.x PubMedGoogle Scholar
  22. 22.
    Cancer Genome Atlas Network (2012) Comprehensive molecular characterization of human colon and rectal cancer. Nature 487:330–337. doi: 10.1038/nature11252 Google Scholar
  23. 23.
    Cantone I, Marucci L, Iorio F et al (2009) A yeast synthetic network for in vivo assessment of reverse-engineering and modeling approaches. Cell 137:172–181. doi: 10.1016/j.cell.2009.01.055 PubMedGoogle Scholar
  24. 24.
    Castelo R, Roverato A (2009) Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J Comput Biol 16:213–227. doi: 10.1089/cmb.2008.08TT PubMedGoogle Scholar
  25. 25.
    Cerami EG, Gross BE, Demir E et al (2011) Pathway commons, a web resource for biological pathway data. Nucleic Acids Res 39:D685–D690. doi: 10.1093/nar/gkq1039 PubMedCentralPubMedGoogle Scholar
  26. 26.
    Chan ECY, Koh PK, Mal M et al (2008) Metabolic profiling of human colorectal cancer using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy and gas chromatography mass spectrometry (GC/MS). J Proteome Res 8:352–361. doi: 10.1021/pr8006232 Google Scholar
  27. 27.
    Chaouiya C, Naldi A, Thieffry D (2012) Logical modelling of gene regulatory networks with GINsim. Methods Mol Biol 804:463–479. doi:PubMedGoogle Scholar
  28. 28.
    Chen KC, Csikasz-Nagy A, Gyorffy B et al (2000) Kinetic analysis of a molecular model of the budding yeast cell cycle. Mol Biol Cell 11:369–391PubMedCentralPubMedGoogle Scholar
  29. 29.
    Cline MS, Smoot M, Cerami E et al (2007) Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 2:2366–2382. doi: 10.1038/nprot.2007.324 PubMedCentralPubMedGoogle Scholar
  30. 30.
    Cloonan N, Forrest ARR, Kolle G et al (2008) Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods 5:613–619. doi: 10.1038/nmeth.1223 PubMedGoogle Scholar
  31. 31.
    Cock PJA, Antao T, Chang JT et al (2009) Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 25:1422–1423. doi: 10.1093/bioinformatics/btp163 PubMedCentralPubMedGoogle Scholar
  32. 32.
    Courtot M, Juty N, Knüpfer C et al (2011) Controlled vocabularies and semantics in systems biology. Mol Syst Biol 7:543. doi: 10.1038/msb.2011.77 PubMedCentralPubMedGoogle Scholar
  33. 33.
    Cox J, Mann M (2011) Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem 80:273–299. doi: 10.1146/annurev-biochem-061308-093216 PubMedGoogle Scholar
  34. 34.
    Croft D, O’Kelly G, Wu G et al (2011) Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Res 39:D691–D697. doi: 10.1093/nar/gkq1018 PubMedCentralPubMedGoogle Scholar
  35. 35.
    Csardi G, Nepusz T (2006) The igraph software package for complex network research. Int J Complex Syst 1695Google Scholar
  36. 36.
    Cvijovic M, Olivares-Hernandez R, Agren R et al (2010) BioMet Toolbox: genome-wide analysis of metabolism. Nucleic Acids Res 38:W144–W149. doi: 10.1093/nar/gkq404 PubMedCentralPubMedGoogle Scholar
  37. 37.
    Demir E, Cary MP, Paley S et al (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28:935–942. doi: 10.1038/nbt.1666 PubMedCentralPubMedGoogle Scholar
  38. 38.
    Deutsch EW, Lam H, Aebersold R (2008) PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep 9:429–434. doi: 10.1038/embor.2008.56 PubMedCentralPubMedGoogle Scholar
  39. 39.
    Dieterle F, Riefke B, Schlotterbeck G, et al. (2011) NMR and MS methods for metabonomics. In: Gautier J-C, Walker JM (eds) Drug safety evaluation. Humana Press, New York, pp 385–415Google Scholar
  40. 40.
    Domon B, Aebersold R (2010) Options and considerations when selecting a quantitative proteomics strategy. Nat Biotechnol 28:710–721. doi: 10.1038/nbt.1661 PubMedGoogle Scholar
  41. 41.
    Durinck S, Moreau Y, Kasprzyk A et al (2005) BioMart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21:3439–3440. doi: 10.1093/bioinformatics/bti525 PubMedGoogle Scholar
  42. 42.
    Dysvik B, Jonassen I (2001) J-Express: exploring gene expression data using Java. Bioinformatics 17:369–370. doi: 10.1093/bioinformatics/17.4.369 PubMedGoogle Scholar
  43. 43.
    Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucl Acids Res 30:207–210. doi: 10.1093/nar/30.1.207 PubMedCentralPubMedGoogle Scholar
  44. 44.
    Edwards JS, Ibarra RU, Palsson BO (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19:125. doi: 10.1038/84379 PubMedGoogle Scholar
  45. 45.
    Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:14863–14868PubMedCentralPubMedGoogle Scholar
  46. 46.
    Fauré A, Naldi A, Chaouiya C, Thieffry D (2006) Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Bioinformatics 22:e124–e131. doi: 10.1093/bioinformatics/btl210 PubMedGoogle Scholar
  47. 47.
    Finger JH, Smith CM, Hayamizu TF et al (2010) The mouse Gene Expression Database (GXD): 2011 update. Nucl Acids Res. doi: 10.1093/nar/gkq1132 Google Scholar
  48. 48.
    Flicek P, Amode MR, Barrell D et al (2011) Ensembl 2012. Nucleic Acids Res 40:D84–D90. doi: 10.1093/nar/gkr991 PubMedCentralPubMedGoogle Scholar
  49. 49.
    Forbes SA, Bindal N, Bamford S et al (2010) COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res 39:D945–D950. doi: 10.1093/nar/gkq929 PubMedCentralPubMedGoogle Scholar
  50. 50.
    Freeman L (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41Google Scholar
  51. 51.
    Funahashi A, Matsuoka Y, Jouraku A et al (2008) Cell designer 3.5: a versatile modeling tool for biochemical networks. Proc IEEE 96:1254–1265. doi: 10.1109/JPROC.2008.925458 Google Scholar
  52. 52.
    Gehlenborg N, O’Donoghue SI, Baliga NS et al (2010) Visualization of omics data for systems biology. Nat Methods 7:S56–S68. doi: 10.1038/nmeth.1436 PubMedGoogle Scholar
  53. 53.
    Gentleman RC, Carey VJ, Bates DM et al (2004) Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5:R80. doi: 10.1186/gb-2004-5-10-r80 PubMedCentralPubMedGoogle Scholar
  54. 54.
    Ghosh S, Matsuoka Y, Asai Y et al (2011) Software for systems biology: from tools to integrated platforms. Nat Rev Genet 12:821–832. doi: 10.1038/nrg3096 PubMedGoogle Scholar
  55. 55.
    Gizzatkulov NM, Goryanin II, Metelkin EA et al (2010) DBSolve Optimum: a software package for kinetic modeling which allows dynamic visualization of simulation results. BMC Syst Biol 4:109. doi: 10.1186/1752-0509-4-109 PubMedCentralPubMedGoogle Scholar
  56. 56.
    Hache H, Lehrach H, Herwig R (2009) Reverse engineering of gene regulatory networks: a comparative study. EURASIP J Bioinf Syst Biol 2009:617281. doi: 10.1155/2009/617281 Google Scholar
  57. 57.
    Hache H, Wierling C, Lehrach H, Herwig R (2009) GeNGe: systematic generation of gene regulatory networks. Bioinformatics 25:1205–1207. doi: 10.1093/bioinformatics/btp115 PubMedCentralPubMedGoogle Scholar
  58. 58.
    Haider S, Ballester B, Smedley D et al (2009) BioMart Central Portal—unified access to biological data. Nucleic Acids Res 37:W23–W27PubMedCentralPubMedGoogle Scholar
  59. 59.
    Hammerman PS, Hayes DN, Wilkerson MD et al (2012) Comprehensive genomic characterization of squamous cell lung cancers. Nature 489:519–525. doi: 10.1038/nature11404 Google Scholar
  60. 60.
    He F, Balling R, Zeng A-P (2009) Reverse engineering and verification of gene networks: principles, assumptions, and limitations of present methods and future perspectives. J Biotechnol 144:190–203. doi: 10.1016/j.jbiotec.2009.07.013 PubMedGoogle Scholar
  61. 61.
    Hecker M, Lambeck S, Toepfer S et al (2009) Gene regulatory network inference: data integration in dynamic models-a review. BioSystems 96:86–103. doi: 10.1016/j.biosystems.2008.12.004 PubMedGoogle Scholar
  62. 62.
    Heinrich R, Rapoport TA (1974) A linear steady-state treatment of enzymatic chains. Eur J Biochem 42:89–95. doi: 10.1111/j.1432-1033.1974.tb03318.x PubMedGoogle Scholar
  63. 63.
    Higham CF (2009) Bifurcation analysis informs Bayesian inference in the Hes1 feedback loop. BMC Syst Biol 3:12. doi: 10.1186/1752-0509-3-12 PubMedCentralPubMedGoogle Scholar
  64. 64.
    Hillier LW, Marth GT, Quinlan AR et al (2008) Whole-genome sequencing and variant discovery in C. elegans. Nat Methods 5:183–188. doi: 10.1038/nmeth.1179 PubMedGoogle Scholar
  65. 65.
    Ho DWY, Yang ZF, Yi K et al (2012) Gene expression profiling of liver cancer stem cells by RNA-sequencing. PLoS ONE 7:e37159. doi: 10.1371/journal.pone.0037159 PubMedCentralPubMedGoogle Scholar
  66. 66.
    Holmes E, Loo RL, Stamler J et al (2008) Human metabolic phenotype diversity and its association with diet and blood pressure. Nature 453:396–400. doi: 10.1038/nature06882 PubMedGoogle Scholar
  67. 67.
    Hoops S, Sahle S, Gauges R et al (2006) COPASI–a COmplex PAthway SImulator. Bioinformatics 22:3067–3074. doi: 10.1093/bioinformatics/btl485 PubMedGoogle Scholar
  68. 68.
    Hu Z, Hung J-H, Wang Y et al (2009) VisANT 3.5: multi-scale network visualization, analysis and inference based on the gene ontology. Nucleic Acids Res 37:W115–W121. doi: 10.1093/nar/gkp406 PubMedCentralPubMedGoogle Scholar
  69. 69.
    Hu Z, Mellor J, Wu J et al (2005) VisANT: data-integrating visual framework for biological networks and modules. Nucleic Acids Res 33:W352–W357. doi: 10.1093/nar/gki431 PubMedCentralPubMedGoogle Scholar
  70. 70.
    Hubble J, Demeter J, Jin H et al (2009) Implementation of genepattern within the Stanford microarray database. Nucleic Acids Res 37:D898–D901. doi: 10.1093/nar/gkn786 PubMedCentralPubMedGoogle Scholar
  71. 71.
    Hucka M, Finney A, Sauro HM et al (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19:524–531PubMedGoogle Scholar
  72. 72.
    Hughes TR, Marton MJ, Jones AR et al (2000) Functional discovery via a compendium of expression profiles. Cell 102:109–126PubMedGoogle Scholar
  73. 73.
    Hunter J (2007) Matplotlib: a 2D Graphics Environment. Comput Sci Eng 9:90–95Google Scholar
  74. 74.
    Ingram PJ, Stumpf MP, Stark J (2006) Network motifs: structure does not determine function. BMC Genomics 7:108. doi: 10.1186/1471-2164-7-108 PubMedCentralPubMedGoogle Scholar
  75. 75.
    Jiang C, Xuan Z, Zhao F, Zhang MQ (2007) TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35:D137–D140. doi: 10.1093/nar/gkl1041 PubMedCentralPubMedGoogle Scholar
  76. 76.
    Jones E, Oliphant T, Peterson P (2001) SciPy: open source scientific tools for Python. In: http://www.scipy.org/. http://www.scipy.org/Citing_SciPy. Accessed 6 Aug 2012
  77. 77.
    de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 9:67–103. doi: 10.1089/10665270252833208 PubMedGoogle Scholar
  78. 78.
    Junker BH, Klukas C, Schreiber F (2006) VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 7:109. doi: 10.1186/1471-2105-7-109 PubMedCentralPubMedGoogle Scholar
  79. 79.
    Kacser H, Burns JA (1973) The control of flux. Symp Soc Exp Biol 27:65–104PubMedGoogle Scholar
  80. 80.
    Kamburov A, Pentchev K, Galicka H et al (2011) ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Res 39:D712–D717. doi: 10.1093/nar/gkq1156 PubMedCentralPubMedGoogle Scholar
  81. 81.
    Kamburov A, Wierling C, Lehrach H, Herwig R (2009) ConsensusPathDB–a database for integrating human functional interaction networks. Nucleic Acids Res 37:D623–D628. doi: 10.1093/nar/gkn698 PubMedCentralPubMedGoogle Scholar
  82. 82.
    Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucl Acids Res 28:27–30. doi: 10.1093/nar/28.1.27 PubMedCentralPubMedGoogle Scholar
  83. 83.
    Kanehisa M, Goto S, Sato Y et al (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109–D114. doi: 10.1093/nar/gkr988 PubMedCentralPubMedGoogle Scholar
  84. 84.
    Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14:491–496. doi: 10.1016/j.copbio.2003.08.001 PubMedGoogle Scholar
  85. 85.
    Kitano H (2004) Biological robustness. Nat Rev Genet 5:826–837. doi: 10.1038/nrg1471 PubMedGoogle Scholar
  86. 86.
    Kitano H (2004) Cancer as a robust system: implications for anticancer therapy. Nat Rev Cancer 4:227–235. doi: 10.1038/nrc1300 PubMedGoogle Scholar
  87. 87.
    Klamt S, Saez-Rodriguez J, Gilles ED (2007) Structural and functional analysis of cellular networks with Cell NetAnalyzer. BMC Syst Biol 1:2. doi: 10.1186/1752-0509-1-2 PubMedCentralPubMedGoogle Scholar
  88. 88.
    Klipp E, Liebermeister W, Wierling C et al (2009) Systems biology: a textbook. Wiley-VCH, WeinheimGoogle Scholar
  89. 89.
    Koboldt DC, Fulton RS, McLellan MD et al (2012) Comprehensive molecular portraits of human breast tumours. Nature. doi: 10.1038/nature11412 Google Scholar
  90. 90.
    Kolker E, Higdon R, Haynes W et al (2011) MOPED: model organism protein expression database. Nucleic Acids Res 40:D1093–D1099. doi: 10.1093/nar/gkr1177 PubMedCentralPubMedGoogle Scholar
  91. 91.
    Kuchaiev O, Stevanović A, Hayes W, Pržulj N (2011) GraphCrunch 2: software tool for network modeling, alignment and clustering. BMC Bioinform 12:24. doi: 10.1186/1471-2105-12-24 Google Scholar
  92. 92.
    Kwon Y-K, Cho K-H (2008) Quantitative analysis of robustness and fragility in biological networks based on feedback dynamics. Bioinformatics 24:987–994. doi: 10.1093/bioinformatics/btn060 PubMedGoogle Scholar
  93. 93.
    Lee JM, Gianchandani EP, Papin JA (2006) Flux balance analysis in the era of metabolomics. Brief Bioinform 7:140–150. doi: 10.1093/bib/bbl007 PubMedGoogle Scholar
  94. 94.
    Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT, Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B, Wyrick JJ, Tagne J-B, Volkert TL, Fraenkel E, Gifford DK, et al (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298:799–804Google Scholar
  95. 95.
    Li C, Donizelli M, Rodriguez N et al (2010) BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol 4:92. doi: 10.1186/1752-0509-4-92 PubMedCentralPubMedGoogle Scholar
  96. 96.
    Li N, Ye M, Li Y et al (2010) Whole genome DNA methylation analysis based on high throughput sequencing technology. Methods 52:203–212. doi: 10.1016/j.ymeth.2010.04.009 PubMedGoogle Scholar
  97. 97.
    Lipson D, Raz T, Kieu A et al (2009) Quantification of the yeast transcriptome by single-molecule sequencing. Nat Biotechnol 27:652–658. doi: 10.1038/nbt.1551 PubMedGoogle Scholar
  98. 98.
    Lloyd CM, Halstead MDB, Nielsen PF (2004) CellML: its future, present and past. Prog Biophys Mol Biol 85:433–450. doi: 10.1016/j.pbiomolbio.2004.01.004 PubMedGoogle Scholar
  99. 99.
    Lloyd CM, Lawson JR, Hunter PJ, Nielsen PF (2008) The CellML model repository. Bioinformatics 24:2122–2123. doi: 10.1093/bioinformatics/btn390 PubMedGoogle Scholar
  100. 100.
    Ma’ayan A (2008) Network integration and graph analysis in mammalian molecular systems biology. Systems Biology, IET 2:206–221. doi: 10.1049/iet-syb:20070075 Google Scholar
  101. 101.
    Ma’ayan A (2011) Introduction to Network Analysis in Systems Biology. Sci Signal 4:tr5. doi: 10.1126/scisignal.2001965
  102. 102.
    Ma’ayan A (2009) Insights into the organization of biochemical regulatory networks using graph theory analyses. J Biol Chem 284:5451–5455. doi: 10.1074/jbc.R800056200 PubMedCentralPubMedGoogle Scholar
  103. 103.
    Mangan S, Alon U (2003) Structure and function of the feed-forward loop network motif. Proc Natl Acad Sci U S A 100:11980–11985. doi: 10.1073/pnas.2133841100 PubMedCentralPubMedGoogle Scholar
  104. 104.
    Marbach D, Costello JC, Küffner R et al (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9:796–804. doi: 10.1038/nmeth.2016 PubMedCentralPubMedGoogle Scholar
  105. 105.
    Marbach D, Prill RJ, Schaffter T et al (2010) Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci U S A 107:6286–6291. doi: 10.1073/pnas.0913357107 PubMedCentralPubMedGoogle Scholar
  106. 106.
    Margolin AA, Wang K, Lim WK et al (2006) Reverse engineering cellular networks. Nat Protoc 1:662–671. doi: 10.1038/nprot.2006.106 PubMedGoogle Scholar
  107. 107.
    Matthews L, Gopinath G, Gillespie M et al (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 37:D619–D622. doi: 10.1093/nar/gkn863 PubMedCentralPubMedGoogle Scholar
  108. 108.
    Matys V, Kel-Margoulis OV, Fricke E et al (2006) TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res 34:D108–D110. doi: 10.1093/nar/gkj143 PubMedCentralPubMedGoogle Scholar
  109. 109.
    Mendes P (1993) GEPASI: a software package for modelling the dynamics, steady states and control of biochemical and other systems. Comput Appl Biosci 9:563–571PubMedGoogle Scholar
  110. 110.
    Mendes P (1997) Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem Sci 22:361–363PubMedGoogle Scholar
  111. 111.
    Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D & Alon U (2002) Network motifs: simple building blocks of complex networks. Science 298:824–827Google Scholar
  112. 112.
    Moreno-Sánchez R, Saavedra E, Rodríguez-Enríquez S, Olín-Sandoval V (2008) Metabolic control analysis: a tool for designing strategies to manipulate metabolic pathways. J Biomed Biotechnol 2008:1–31. doi: 10.1155/2008/597913 Google Scholar
  113. 113.
    Nagalakshmi U, Wang Z, Waern K et al (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320:1344–1349. doi: 10.1126/science.1158441 PubMedCentralPubMedGoogle Scholar
  114. 114.
    Nagaraj N, Wisniewski JR, Geiger T et al (2011) Deep proteome and transcriptome mapping of a human cancer cell line. Molecular Systems Biology. doi: 10.1038/msb.2011.81 PubMedCentralPubMedGoogle Scholar
  115. 115.
    Narendra V, Lytkin NI, Aliferis CF, Statnikov A (2011) A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks. Genomics 97:7–18. doi: 10.1016/j.ygeno.2010.10.003 PubMedCentralPubMedGoogle Scholar
  116. 116.
    Nicholson JK, Lindon JC (2008) Systems biology: metabonomics. Nature 455:1054–1056. doi: 10.1038/4551054a PubMedGoogle Scholar
  117. 117.
    Nilsson T, Mann M, Aebersold R et al (2010) Mass spectrometry in high-throughput proteomics: ready for the big time. Nat Methods 7:681–685. doi: 10.1038/nmeth0910-681 PubMedGoogle Scholar
  118. 118.
    Le Novère N, Hucka M, Mi H et al (2009) The systems biology graphical notation. Nat Biotechnol 27:735–741. doi: 10.1038/nbt.1558 PubMedGoogle Scholar
  119. 119.
    Le Novère N, Bornstein B, Broicher A et al (2006) BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 34:D689–D691. doi: 10.1093/nar/gkj092 PubMedCentralPubMedGoogle Scholar
  120. 120.
    Odom DT, Zizlsperger N, Gordon DB, Bell GW, Rinaldi NJ, Murray HL, Volkert TL, Schreiber J, Rolfe PA, Gifford DK, Fraenkel E, Bell GI & Young RA (2004) Control of pancreas and liver gene expression by HNF transcription factors. Science 303:1378–1381Google Scholar
  121. 121.
    Olivier BG, Snoep JL (2004) Web-based kinetic modelling using JWS Online. Bioinformatics 20:2143–2144. doi: 10.1093/bioinformatics/bth200 PubMedGoogle Scholar
  122. 122.
    Opgen-Rhein R, Strimmer K (2007) From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol 1:37. doi: 10.1186/1752-0509-1-37 PubMedCentralPubMedGoogle Scholar
  123. 123.
    Orth JD, Thiele I, Palsson BØ (2010) What is flux balance analysis? Nat Biotechnol 28:245. doi: 10.1038/nbt.1614 PubMedCentralPubMedGoogle Scholar
  124. 124.
    Parkinson H, Sarkans U, Kolesnikov N et al (2010) ArrayExpress update–an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res 39:D1002–D1004. doi: 10.1093/nar/gkq1040 PubMedCentralPubMedGoogle Scholar
  125. 125.
    Perumal TM, Gunawan R (2011) Understanding dynamics using sensitivity analysis: caveat and solution. BMC Syst Biol 5:41. doi: 10.1186/1752-0509-5-41 PubMedCentralPubMedGoogle Scholar
  126. 126.
    Picotti P, Bodenmiller B, Mueller LN et al (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138:795–806. doi: 10.1016/j.cell.2009.05.051 PubMedCentralPubMedGoogle Scholar
  127. 127.
    Pleasance ED, Cheetham RK, Stephens PJ et al (2009) A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463:191–196. doi: 10.1038/nature08658 PubMedCentralPubMedGoogle Scholar
  128. 128.
    Pontén F, Gry M, Fagerberg L et al (2009) A global view of protein expression in human cells, tissues, and organs. Mol Syst Biol. doi: 10.1038/msb.2009.93 PubMedCentralPubMedGoogle Scholar
  129. 129.
    Prasad TV, Babu RP, Ahson SI (2006) GEDAS—gene expression data analysis suite. Bioinformation 1:83–85PubMedCentralPubMedGoogle Scholar
  130. 130.
    Prill RJ, Marbach D, Saez-Rodriguez J et al (2010) Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE 5:e9202. doi: 10.1371/journal.pone.0009202 PubMedCentralPubMedGoogle Scholar
  131. 131.
    R Development Core Team (2012) R: a language and environment for statistical computingGoogle Scholar
  132. 132.
    Ramakrishna R, Edwards JS, McCulloch A, Palsson BO (2001) Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints. Am J Physiol Regul Integr Comp Physiol 280:R695–R704PubMedGoogle Scholar
  133. 133.
    Raman K, Chandra N (2009) Flux balance analysis of biological systems: applications and challenges. Brief Bioinform 10:435–449. doi: 10.1093/bib/bbp011 PubMedGoogle Scholar
  134. 134.
    Reich M, Liefeld T, Gould J et al (2006) GenePattern 2.0. Nat Genet 38:500–501. doi: 10.1038/ng0506-500 PubMedGoogle Scholar
  135. 135.
    Rodriguez-Fernandez M, Banga JR (2010) SensSB: a software toolbox for the development and sensitivity analysis of systems biology models. Bioinformatics 26:1675–1676. doi: 10.1093/bioinformatics/btq242 PubMedGoogle Scholar
  136. 136.
    Sabidussi G (1966) The centrality index of a graph. Psychometrika 31:581–603. doi: 10.1007/BF02289527 PubMedGoogle Scholar
  137. 137.
    Saltelli A, Chan K, Scott E (2000) Sensitivity analysis. Wiley, ChichesterGoogle Scholar
  138. 138.
    Saltelli A, Ratto M, Andres T et al (2008) Global sensitivity analysis: the primer. Wiley, ChichesterGoogle Scholar
  139. 139.
    Saraç ÖS, Pancaldi V, Bähler J, Beyer A (2012) Topology of functional networks predicts physical binding of proteins. Bioinformatics. doi: 10.1093/bioinformatics/bts351 PubMedGoogle Scholar
  140. 140.
    Scheer M, Grote A, Chang A et al (2011) BRENDA, the enzyme information system in 2011. Nucleic Acids Res 39:D670–D676. doi: 10.1093/nar/gkq1089 PubMedCentralPubMedGoogle Scholar
  141. 141.
    Schena M, Shalon D, Davis RW, Brown PO (1995) Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270:467–740PubMedGoogle Scholar
  142. 142.
    Schmidt H, Jirstrand M (2006) Systems Biology Toolbox for MATLAB: a computational platform for research in systems biology. Bioinformatics 22:514–515. doi: 10.1093/bioinformatics/bti799 PubMedGoogle Scholar
  143. 143.
    Schuster S, Dandekar T, Fell DA (1999) Detection of elementary flux modes in biochemical networks: a promising tool for pathway analysis and metabolic engineering. Trends Biotechnol 17:53–60PubMedGoogle Scholar
  144. 144.
    Seal RL, Gordon SM, Lush MJ et al (2011) genenames.org: the HGNC resources in 2011. Nucleic Acids Res 39:D514–D519. doi: 10.1093/nar/gkq892 PubMedCentralPubMedGoogle Scholar
  145. 145.
    Shaham O, Wei R, Wang TJ et al (2008) Metabolic profiling of the human response to a glucose challenge reveals distinct axes of insulin sensitivity. Molecular Systems Biology. doi: 10.1038/msb.2008.50 PubMedCentralPubMedGoogle Scholar
  146. 146.
    Shannon P, Markiel A, Ozier O et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. doi: 10.1101/gr.1239303 PubMedCentralPubMedGoogle Scholar
  147. 147.
    Sharan R, Maron-Katz A, Shamir R (2003) CLICK and EXPANDER: a system for clustering and visualizing gene expression data. Bioinformatics 19:1787–1799. doi: 10.1093/bioinformatics/btg232 PubMedGoogle Scholar
  148. 148.
    Shen-Orr SS, Milo R, Mangan S & Alon U (2002) Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31:64–68Google Scholar
  149. 149.
    Shin G, Kang T-W, Yang S et al (2011) GENT: gene expression database of normal and tumor tissues. Cancer Inform 10:149–157. doi: 10.4137/CIN.S7226 PubMedCentralPubMedGoogle Scholar
  150. 150.
    Shlomi T, Berkman O, Ruppin E (2005) Regulatory on/off minimization of metabolic flux changes after genetic perturbations. PNAS 102:7695–7700. doi: 10.1073/pnas.0406346102 PubMedCentralPubMedGoogle Scholar
  151. 151.
    Siek J, Lee L-Q, Lumsdaine A (2001) The boost graph library: user guide and reference manual (C++ In-Depth Series). Addison-Wesley ProfessionalGoogle Scholar
  152. 152.
    Smoot ME, Ono K, Ruscheinski J et al (2011) Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics 27:431–432. doi: 10.1093/bioinformatics/btq675 PubMedCentralPubMedGoogle Scholar
  153. 153.
    Soranzo N, Bianconi G, Altafini C (2007) Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics 23:1640–1647. doi: 10.1093/bioinformatics/btm163 PubMedGoogle Scholar
  154. 154.
    Spellman PT, Sherlock G, Zhang MQ et al (1998) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell 9:3273–3297PubMedCentralPubMedGoogle Scholar
  155. 155.
    Stolovitzky G, Monroe D, Califano A (2007) Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann N Y Acad Sci 1115:1–22. doi: 10.1196/annals.1407.021 PubMedGoogle Scholar
  156. 156.
    Stolovitzky G, Prill RJ, Califano A (2009) Lessons from the DREAM2 Challenges. Ann N Y Acad Sci 1158:159–195. doi: 10.1111/j.1749-6632.2009.04497.x PubMedGoogle Scholar
  157. 157.
    Sugimoto M, Ikeda S, Niigata K et al (2011) MMMDB: mouse multiple tissue metabolome database. Nucleic Acids Res 40:D809–D814. doi: 10.1093/nar/gkr1170 PubMedCentralPubMedGoogle Scholar
  158. 158.
    Sultan M, Schulz MH, Richard H et al (2008) A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science 321:956–960. doi: 10.1126/science.1160342 PubMedGoogle Scholar
  159. 159.
    Tanabe M, Kanehisa M (2012) Using the KEGG Database Resource. Curr Protoc Bioinformatics Chapter 1: Unit1.12. doi: 10.1002/0471250953.bi0112s38
  160. 160.
    Uhlen M, Oksvold P, Fagerberg L et al (2010) Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 28:1248–1250. doi: 10.1038/nbt1210-1248 PubMedGoogle Scholar
  161. 161.
    Visco C, Li Y, Xu-Monette ZY et al (2012) Comprehensive gene expression profiling and immunohistochemical studies support application of immunophenotypic algorithm for molecular subtype classification in diffuse large B-cell lymphoma: a report from the International DLBCL Rituximab-CHOP Consortium Program Study. Leukemia. doi: 10.1038/leu.2012.83 PubMedCentralPubMedGoogle Scholar
  162. 162.
    Visel A, Blow MJ, Li Z et al (2009) ChIP-seq accurately predicts tissue-specific activity of enhancers. Nature 457:854–858. doi: 10.1038/nature07730 PubMedCentralPubMedGoogle Scholar
  163. 163.
    Wang J, Wang W, Li R et al (2008) The diploid genome sequence of an Asian individual. Nature 456:60–65. doi: 10.1038/nature07484 PubMedCentralPubMedGoogle Scholar
  164. 164.
    Wang M, Weiss M, Simonovic M et al (2012) PaxDb, a database of protein abundance averages across all three domains of life. Mol Cell Proteomics. doi: 10.1074/mcp.O111.014704 Google Scholar
  165. 165.
    Wang Y-C, Chen B-S (2010) Integrated cellular network of transcription regulations and protein–protein interactions. BMC Syst Biol 4:20. doi: 10.1186/1752-0509-4-20 PubMedCentralPubMedGoogle Scholar
  166. 166.
    Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63. doi: 10.1038/nrg2484 PubMedCentralPubMedGoogle Scholar
  167. 167.
    Watts DJ, Strogatz SH (1998) Collective dynamics of “small-world” networks. Nature 393:440–442. doi: 10.1038/30918 PubMedGoogle Scholar
  168. 168.
    Werhli AV, Grzegorczyk M, Husmeier D (2006) Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 22:2523–2531. doi: 10.1093/bioinformatics/btl391 PubMedGoogle Scholar
  169. 169.
    Wheeler DA, Srinivasan M, Egholm M et al (2008) The complete genome of an individual by massively parallel DNA sequencing. Nature 452:872–876. doi: 10.1038/nature06884 PubMedGoogle Scholar
  170. 170.
    Whitfield ML, Sherlock G, Saldanha AJ et al (2002) Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol Biol Cell 13:1977–2000. doi: 10.1091/mbc.02-02-0030 PubMedCentralPubMedGoogle Scholar
  171. 171.
    Wierling C, Herwig R, Lehrach H (2007) Resources, standards and tools for systems biology. Brief Funct Genomic Proteomic 6:240–251. doi: 10.1093/bfgp/elm027 PubMedGoogle Scholar
  172. 167.
    Wildermuth MC (2000) Metabolic control analysis: biological applications and insights. Genome Biol 1: reviews1031. doi: 10.1186/gb-2000-1-6-reviews1031
  173. 173.
    Wilhelm BT, Marguerat S, Watt S et al (2008) Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453:1239–1243. doi: 10.1038/nature07002 PubMedGoogle Scholar
  174. 174.
    Wishart DS, Knox C, Guo AC et al (2009) HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37:D603–D610. doi: 10.1093/nar/gkn810 PubMedCentralPubMedGoogle Scholar
  175. 175.
    Wittig U, Kania R, Golebiewski M et al (2012) SABIO-RK–database for biochemical reaction kinetics. Nucleic Acids Res 40:D790–D796. doi: 10.1093/nar/gkr1046 PubMedCentralPubMedGoogle Scholar
  176. 176.
    Wu WH, Wang FS, Chang MS (2008) Dynamic sensitivity analysis of biological systems. BMC Bioinformatics 9:S17. doi: 10.1186/1471-2105-9-S12-S17 PubMedCentralPubMedGoogle Scholar
  177. 177.
    Wunderlich Z, Mirny LA (2006) Using the topology of metabolic networks to predict viability of mutant strains. Biophys J 91:2304–2311. doi: 10.1529/biophysj.105.080572 PubMedCentralPubMedGoogle Scholar
  178. 178.
    Xu J, Li Y (2006) Discovering disease-genes by topological features in human protein–protein interaction network. Bioinformatics 22:2800–2805. doi: 10.1093/bioinformatics/btl467 PubMedGoogle Scholar
  179. 179.
    Yu H, Greenbaum D, Xin LuH et al (2004) Genomic analysis of essentiality within protein networks. Trends Genet 20:227–231. doi: 10.1016/j.tig.2004.04.008 PubMedGoogle Scholar
  180. 180.
    Zaslaver A, Mayo AE, Rosenberg R et al (2004) Just-in-time transcription program in metabolic pathways. Nat Genet 36:486–491. doi: 10.1038/ng1348 PubMedGoogle Scholar
  181. 181.
    Zhao W, Serpedin E, Dougherty ER (2008) Recovering Genetic Regulatory Networks from Chromatin Immunoprecipitation and Steady-State Microarray Data. EURASIP J Bioinf Syst Biol 2008:248747. doi: 10.1155/2008/248747 Google Scholar
  182. 182.
    Zhu M, Gao L, Li X et al (2009) The analysis of the drug-targets based on the topological properties in the human protein–protein interaction network. J Drug Target 17:524–532. doi: 10.1080/10611860903046610 PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Max Planck Institute for Molecular GeneticsBerlinGermany

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