Skip to main content
SpringerLink
Log in
Book cover

Theoretical and Applied Aspects of Systems Biology pp 221–242Cite as

System Biology to Access Target Relevance in the Research and Development of Molecular Inhibitors

  • Chapter
  • First Online:

Part of the book series: Computational Biology ((COBO,volume 27))

Abstract

This review focuses on how system biology may assist techniques that are used in pharmacological research, such as high-throughput screening, high-throughput analytical characterization of biological samples, preclinical and clinical trials, as well as targets and drug validation in order to reach patients at the lowest possible cost in a translational perspective. In signaling networks, targets can be assessed through topological criteria such as their connectivity and/or centrality. In metabolic networks, the relevance of a target for drug development may rather be assessed through some sort of enzymatic specificity resulting from remote homology, analogy, or specificity in its strict sense. The concept of specificity is especially valuable in the context of a host-parasite relationship where targeting a protein specific of a parasite compared to its host is expected to minimize the noxious collateral effects of the inhibitor to the host. The relevance of putative molecular target must be proven through bench and animal validations prior to going through clinical trials. Flux balance analysis and other modeling methods of system biology enable to assess whether a molecular target can be considered as pathway’s choke or not in a network context, which may facilitate the decision of developing drugs for it.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Knight-Schrijver VR, Chelliah V, Cucurull-Sanchez L, Le Novère N. The promises of quantitative systems pharmacology modelling for drug development. Comput Struct Biotechnol J. 2016;14:363–70.

    Article  Google Scholar 

  2. Arrowsmith J. A decade of change. Nat Rev Drug Discov. 2012;11(1):17–8.

    Article  Google Scholar 

  3. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov. 2010;9(3):203–14.

    Article  Google Scholar 

  4. Butcher EC. Can cell systems biology rescue drug discovery? Nat Rev Drug Discov. 2005;4(6):461–7.

    Article  Google Scholar 

  5. Osterloh IH. The discovery and development of Viagra® (sildenafil citrate). In: Dunzendorfer U, editor. Sildenafil. Milestones in drug therapy MDT. Basel: Birkhäuser; 2004. https://doi.org/10.1007/978-3-0348-7945-3_1.

    Chapter  Google Scholar 

  6. Priest BT, Erdemli G. Phenotypic screening in the 21st century. Front Pharmacol. 2014;5:264.

    Article  Google Scholar 

  7. Kaelin WG Jr. Common pitfalls in preclinical cancer target validation. Nat Rev Cancer. 2017;17:425–40.

    Article  Google Scholar 

  8. Lee J, Bogyo M. Target deconvolution techniques in modern phenotypic profiling. Curr Opin Chem Biol. 2013;17(1):118–26.

    Article  Google Scholar 

  9. Santos R, Ursu O, Gaulton A, Bento AP, Donadi RS, Bologa CG, et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov. 2017;16(1):19–34.

    Article  Google Scholar 

  10. Hopkins AL, Richard Bickerton G, Carruthers IM, Boyer SK, Rubin H, Overington JP. Rapid analysis of pharmacology for infectious diseases. Curr Top Med Chem. 2011;11(10):1292–300.

    Article  Google Scholar 

  11. Soldatow VY, LeCluyse EL, Griffith LG, Rusyn I. In vitro models for liver toxicity testing. Toxicol Res. 2013;2(1):23–39.

    Article  Google Scholar 

  12. Luni C, Serena E, Elvassore N. Human-on-chip for therapy development and fundamental science. Curr Opin Biotechnol. 2014;25:45–50.

    Article  Google Scholar 

  13. Peng W, Unutmaz D, Ozbolat IT. Bioprinting towards physiologically relevant tissue models for pharmaceutics. Trends Biotechnol. 2016;34(9):722–32.

    Article  Google Scholar 

  14. Andrade EL, Bento AF, Cavalli J, Oliveira SK, Freitas CS, Marcon R, et al. Non-clinical studies required for new drug development-part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies. Braz J Med Biol Res. 2016;49(11):e5644.

    Article  Google Scholar 

  15. Denayer T, Stöhr T, Van Roy M. Animal models in translational medicine: validation and prediction. New Horizons Transl Med. 2014;2(1):5–11.

    Google Scholar 

  16. Wermuth CG. Multitargeted drugs: the end of the “one-target-one-disease”philosophy? Drug Discov Today. 2004;9(19):826–7.

    Article  Google Scholar 

  17. Masoudi-Nejad A, Mousavian Z, Bozorgmehr JH. Drug-target and disease networks: polypharmacology in the post-genomic era. In silico Pharmacol. 2013;1(1):17.

    Article  Google Scholar 

  18. Ágoston V, Csermely P, Pongor S. Multiple weak hits confuse complex systems: a transcriptional regulatory network as an example. Phys Rev E. 2005;71(5):51909.

    Article  Google Scholar 

  19. Winter GE, Rix U, Carlson SM, Gleixner KV, Grebien F, Gridling M, et al. Systems-pharmacology dissection of a drug synergy in imatinib-resistant CML. Nat Chem Biol. 2012;8(11):905–12.

    Article  Google Scholar 

  20. Chen S, Jiang H, Cao Y, Wang Y, Hu Z, Zhu Z, et al. Drug target identification using network analysis: taking active components in Sini decoction as an example. Sci Rep. 2016;6. https://doi.org/10.1038/srep24245.

  21. Kinch MS, Haynesworth A, Kinch SL, Hoyer D. An overview of FDA-approved new molecular entities: 1827–2013. Drug Discov Today. 2014;19(8):1033–9.

    Article  Google Scholar 

  22. Winkler H. Target validation requirements in the pharmaceutical industry. Targets. 2003;2(3):69–71.

    Article  Google Scholar 

  23. Milligan PA, Brown MJ, Marchant B, Martin SW, Graaf PH, Benson N, et al. Model-based drug development: a rational approach to efficiently accelerate drug development. Clin Pharmacol Ther. 2013;93(6):502–14.

    Article  Google Scholar 

  24. Visser SAG, Manolis E, Danhof M, Kerbusch T. Modeling and simulation at the interface of nonclinical and early clinical drug development. CPT Pharmacometrics Syst Pharmacol. 2013;2(2):1–3.

    Article  Google Scholar 

  25. Visser SAG, Aurell M, Jones RDO, Schuck VJA, Egnell A-C, Peters SA, et al. Model-based drug discovery: implementation and impact. Drug Discov Today. 2013;18(15):764–75.

    Article  Google Scholar 

  26. Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nat Rev Drug Discov. 2014;13(6):419–31.

    Article  Google Scholar 

  27. Visser SAG, Alwis DP, Kerbusch T, Stone JA, Allerheiligen SRB. Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacometrics Syst Pharmacol. 2014;3(10):1–10.

    Article  Google Scholar 

  28. Kumar N, Hendriks BS, Janes KA, de Graaf D, Lauffenburger DA. Applying computational modeling to drug discovery and development. Drug Discov Today. 2006;11(17):806–11.

    Article  Google Scholar 

  29. Brodland GW. How computational models can help unlock biological systems. Semin Cell Dev Biol. 2015;47–48:62–73.

    Article  Google Scholar 

  30. Bombelles T, Coaker H. Neglected tropical disease research: rethinking the drug discovery model. Future Med Chem. 2015;7(6):693–700.

    Article  Google Scholar 

  31. Al-Lazikani B, Workman P. Unpicking the combination lock for mutant BRAF and RAS melanomas. Cancer Discov. 2013;3(1):14–9.

    Article  Google Scholar 

  32. Workman P, Clarke PA, Al-Lazikani B. Blocking the survival of the nastiest by HSP90 inhibition. Oncotarget. 2016;7(4):3658.

    Article  Google Scholar 

  33. Paolini GV, Shapland RHB, van Hoorn WP, Mason JS, Hopkins AL. Global mapping of pharmacological space. Nat Biotechnol. 2006;24(7):805–15.

    Article  Google Scholar 

  34. Catharina L, Lima CR, Franca A, Guimarães ACR, Alves-Ferreira M, Tuffery P, Derreumaux P, et al. A computational methodology to overcome the challenges associated with the search for specific enzyme targets to develop drugs against. Bioinform Biol Insights. 2017;11. https://doi.org/10.1177/1177932217712471.

  35. Wei D, Jiang X, Zhou L, Chen J, Chen Z, He C, et al. Discovery of multitarget inhibitors by combining molecular docking with common pharmacophore matching. J Med Chem. 2008;51(24):7882–8.

    Article  Google Scholar 

  36. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, et al. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006;34(suppl 1):D668–72.

    Article  Google Scholar 

  37. Günther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, et al. SuperTarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res. 2007;36(suppl_1):D919–22.

    Article  Google Scholar 

  38. Gao Z, Li H, Zhang H, Liu X, Kang L, Luo X, et al. PDTD: a web-accessible protein database for drug target identification. BMC Bioinformatics. 2008;9(1):104.

    Article  Google Scholar 

  39. Hu Z, Chang Y-C, Wang Y, Huang C-L, Liu Y, Tian F, et al. VisANT 4.0: integrative network platform to connect genes, drugs, diseases and therapies. Nucleic Acids Res. 2013;41(W1):W225–31.

    Article  Google Scholar 

  40. Kim Kjærulff S, Wich L, Kringelum J, Jacobsen UP, Kouskoumvekaki I, Audouze K, et al. ChemProt-2.0: visual navigation in a disease chemical biology database. Nucleic Acids Res. 2012;41(D1):D464–9.

    Article  Google Scholar 

  41. Yamanishi Y, Kotera M, Moriya Y, Sawada R, Kanehisa M, Goto S. DINIES: drug–target interaction network inference engine based on supervised analysis. Nucleic Acids Res. 2014;42:W39–45.

    Article  Google Scholar 

  42. Hu Q, Deng Z, Tu W, Yang X, Meng Z, Deng Z, et al. VNP: interactive visual network pharmacology of diseases, targets, and drugs. CPT Pharmacometrics Syst Pharmacol. 2014;3(3):1–8.

    Article  Google Scholar 

  43. Barabasi A-L, Oltvai ZN. Network biology: understanding the cell’s functional organization. Nat Rev Genet. 2004;5(2):101–13.

    Article  Google Scholar 

  44. Yamada T, Bork P. Evolution of biomolecular networks—lessons from metabolic and protein interactions. Nat Rev Mol Cell Biol. 2009;10(11):791–803.

    Article  Google Scholar 

  45. Yıldırım MA, Goh K-I, Cusick ME, Barabási A-L, Vidal M. Drug—target network. Nat Biotechnol. 2007;25(10):1119–26.

    Article  Google Scholar 

  46. Butina D, Segall MD, Frankcombe K. Predicting ADME properties in silico: methods and models. Drug Discov Today. 2002;7(11):S83–8.

    Article  Google Scholar 

  47. Byvatov E, Fechner U, Sadowski J, Schneider G. Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci. 2003;43(6):1882–9.

    Article  Google Scholar 

  48. Khedkar SA, Malde AK, Coutinho EC, Srivastava S. Pharmacophore modeling in drug discovery and development: an overview. Med Chem. 2007;3(2):187–97.

    Article  Google Scholar 

  49. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785–91.

    Article  Google Scholar 

  50. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, et al. DrugBank 3.0: a comprehensive resource for “omics” research on drugs. Nucleic Acids Res. 2010;39:D1035–41.

    Article  Google Scholar 

  51. Wang JT, Liu W, Tang H, Xie H. Screening drug target proteins based on sequence information. J Biomed Inform. 2014;49:269–74.

    Article  Google Scholar 

  52. Li Z, Han P, You Z-H, Li X, Zhang Y, Yu H, et al. In silico prediction of drug-target interaction networks based on drug chemical structure and protein sequences. Sci Rep. 2017;7(1):11174.

    Article  Google Scholar 

  53. Lam MPY, Venkatraman V, Xing Y, Lau E, Cao Q, Ng DCM, et al. Data-driven approach to determine popular proteins for targeted proteomics translation of six organ systems. J Proteome Res. 2016;15(11):4126–34.

    Article  Google Scholar 

  54. Zhu S, Okuno Y, Tsujimoto G, Mamitsuka H. A probabilistic model for mining implicit “chemical compound–gene”relations from literature. Bioinformatics. 2005;21(suppl 2):ii245–51.

    Article  Google Scholar 

  55. Wang Z, Li J, Dang R, Liang L, Lin J. PhIN: a protein pharmacology interaction network database. CPT Pharmacometrics Syst Pharmacol. 2015;4(3):160–6.

    Article  Google Scholar 

  56. Barabási A-L, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56–68.

    Article  Google Scholar 

  57. Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabási A-L. The human disease network. Proc Natl Acad Sci. 2007;104(21):8685–90.

    Article  Google Scholar 

  58. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008;24(13):i232–40.

    Article  Google Scholar 

  59. Stockwell BR. Chemical genetics: ligand-based discovery of gene function. Nat Rev Genet. 2000;1(2):116–25.

    Article  MathSciNet  Google Scholar 

  60. Dobson CM. Chemical space and biology. Nature. 2004;432(7019):824–8.

    Article  Google Scholar 

  61. Kanehisa M, Goto S, Hattori M, Aoki-Kinoshita KF, Itoh M, Kawashima S, et al. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006;34:D354–7.

    Article  Google Scholar 

  62. Kundrotas PJ, Zhu Z, Janin J, Vakser IA. Templates are available to model nearly all complexes of structurally characterized proteins. Proc Natl Acad Sci. 2012;109(24):9438–41.

    Article  Google Scholar 

  63. Duran-Frigola M, Mosca R, Aloy P. Structural systems pharmacology: the role of 3D structures in next-generation drug development. Chem Biol. 2013;20(5):674–84.

    Article  Google Scholar 

  64. Yang JO, Oh S, Ko G, Park S-J, Kim W-Y, Lee B, et al. VnD: a structure-centric database of disease-related SNPs and drugs. Nucleic Acids Res. 2010;39(suppl 1):D939–44.

    Google Scholar 

  65. Preissner S, Kroll K, Dunkel M, Senger C, Goldsobel G, Kuzman D, et al. SuperCYP: a comprehensive database on cytochrome P450 enzymes including a tool for analysis of CYP-drug interactions. Nucleic Acids Res. 2009;38(suppl 1):D237–43.

    Google Scholar 

  66. Fuller JC, Burgoyne NJ, Jackson RM. Predicting druggable binding sites at the protein–protein interface. Drug Discov Today. 2009;14(3):155–61.

    Article  Google Scholar 

  67. Schlecht U, Miranda M, Suresh S, Davis RW, Onge RPS. Multiplex assay for condition-dependent changes in protein–protein interactions. Proc Natl Acad Sci U S A. 2012;109(23):9213–8.

    Article  Google Scholar 

  68. Sperandio O, Reynès CH, Camproux A-C, Villoutreix BO. Rationalizing the chemical space of protein–protein interaction inhibitors. Drug Discov Today. 2010;15(5):220–9.

    Article  Google Scholar 

  69. Koes DR, Camacho CJ. PocketQuery: protein–protein interaction inhibitor starting points from protein–protein interaction structure. Nucleic Acids Res. 2012;40:W387–92.

    Article  Google Scholar 

  70. Taboureau O, Baell JB, Fernández-Recio J, Villoutreix BO. Established and emerging trends in computational drug discovery in the structural genomics era. Chem Biol. 2012;19(1):29–41.

    Article  Google Scholar 

  71. Świderek K, Tuñón I, Moliner V, Bertran J. Computational strategies for the design of new enzymatic functions. Arch Biochem Biophys. 2015;582:68–79.

    Article  Google Scholar 

  72. Copeland RA. Evaluation of enzyme inhibitors in drug discovery: a guide for medicinal chemists and pharmacologists. Hoboken: Wiley; 2013.

    Book  Google Scholar 

  73. Robertson JG. Enzymes as a special class of therapeutic target: clinical drugs and modes of action. Curr Opin Struct Biol. 2007;17(6):674–9.

    Article  Google Scholar 

  74. Copeland RA. The dynamics of drug-target interactions: drug-target residence time and its impact on efficacy and safety. Expert Opin Drug Discov. 2010;5(4):305–10.

    Article  Google Scholar 

  75. Copeland RA. Evaluation of enzyme inhibitors in drug discovery: a guide for medicinal chemists and pharmacologists. New York: Wiley-Interscience; 2005. p. 178–213.

    Google Scholar 

  76. Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;1(9):727–30.

    Article  Google Scholar 

  77. Thomas D. A big year for novel drugs approvals [Internet]. 2013. Available from: http://www.biotech-now.org/business-and-investments/inside-bio-ia/2013/01/a-big-year-for-novel-drugs-approvals.

  78. Bull SC, Doig AJ. Properties of protein drug target classes. PLoS One. 2015;10(3):e0117955.

    Article  Google Scholar 

  79. Burkhard K, Shapiro P. Use of inhibitors in the study of MAP kinases. MAP Kinase Signal Protoc. 2010;661:107–22.

    Article  Google Scholar 

  80. López-Otín C, Bond JS. Proteases: multifunctional enzymes in life and disease. J Biol Chem. 2008;283(45):30433–7.

    Article  Google Scholar 

  81. Rask-Andersen M, Zhang J, Fabbro D, Schiöth HB. Advances in kinase targeting: current clinical use and clinical trials. Trends Pharmacol Sci. 2014;35(11):604–20.

    Article  Google Scholar 

  82. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science. 2002;298(5600):1912–34.

    Article  Google Scholar 

  83. Hanks SK, Hunter T. Protein kinases 6. The eukaryotic protein kinase superfamily: kinase (catalytic) domain structure and classification. FASEB J. 1995;9(8):576–96.

    Article  Google Scholar 

  84. Engh RA, Bossemeyer D. Structural aspects of protein kinase control—role of conformational flexibility. Pharmacol Ther. 2002;93(2):99–111.

    Article  Google Scholar 

  85. Melnikova I, Golden J. Targeting protein kinases. Nat Rev Drug Discov. 2004;3(12):993–4.

    Article  Google Scholar 

  86. Puente XS, Sánchez LM, Overall CM, López-Otín C. Human and mouse proteases: a comparative genomic approach. Nat Rev Genet. 2003;4(7):544–58.

    Article  Google Scholar 

  87. Puente XS, López-Otín C. A genomic analysis of rat proteases and protease inhibitors. Genome Res. 2004;14(4):609–22.

    Article  Google Scholar 

  88. Puente XS, Sanchez LM, Gutierrez-Fernandez A, Velasco G, Lopez-Otin C. A genomic view of the complexity of mammalian proteolytic systems. Biochem Soc Trans. 2005;33(Pt 2):331–4.

    Article  Google Scholar 

  89. Rodríguez D, Morrison CJ, Overall CM. Matrix metalloproteinases: what do they not do? New substrates and biological roles identified by murine models and proteomics. Biochim Biophys Acta. 2010;1803(1):39–54.

    Article  Google Scholar 

  90. Drag M, Salvesen GS. Emerging principles in protease-based drug discovery. Nat Rev Drug Discov. 2010;9(9):690–701.

    Article  Google Scholar 

  91. Duffy MJ, McGowan PM, Gallagher WM. Cancer invasion and metastasis: changing views. J Pathol. 2008;214(3):283–93.

    Article  Google Scholar 

  92. Fauci AS, Morens DM. The perpetual challenge of infectious diseases. N Engl J Med. 2012;366(5):454–61.

    Article  Google Scholar 

  93. Fontana JM, Alexander E, Salvatore M. Translational research in infectious disease: current paradigms and challenges ahead. Transl Res. 2012;159(6):430–53.

    Article  Google Scholar 

  94. Wooller SK, Benstead-Hume G, Chen X, Ali Y, Pearl FMG. Bioinformatics in translational drug discovery. Biosci Rep. 2017;37(4):BSR20160180.

    Article  Google Scholar 

  95. Naccache SN, Federman S, Veeraraghavan N, Zaharia M, Lee D, Samayoa E, et al. A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples. Genome Res. 2014;24(7):1180–92.

    Article  Google Scholar 

  96. Frey KG, Bishop-Lilly KA. Next-generation sequencing for pathogen detection and identification. Methods Microbiol. 2015;42:525–54.

    Article  Google Scholar 

  97. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215(3):403–10.

    Article  Google Scholar 

  98. Carels N, Frias D. A statistical method without training step for the classification of coding frame in transcriptome sequences. Bioinform Biol Insights. 2013;7:35.

    Article  Google Scholar 

  99. Carels N, Gumiel M, da Mota FF, de Carvalho Moreira CJ, Azambuja P. A metagenomic analysis of bacterial microbiota in the digestive tract of triatomines. Bioinform Biol Insights. 2017;11. https://doi.org/10.1177/1177932217733422.

    Article  Google Scholar 

  100. de Castro MR, dos Santos TC, Dávila AMR, Senger H, da Silva FAB. SparkBLAST: scalable BLAST processing using in-memory operations. BMC Bioinformatics. 2017;18(1):318.

    Article  Google Scholar 

  101. Beltran PMJ, Federspiel JD, Sheng X, Cristea IM. Proteomics and integrative omic approaches for understanding host–pathogen interactions and infectious diseases. Mol Syst Biol. 2017;13(3):922.

    Article  Google Scholar 

  102. Flórez AF, Park D, Bhak J, Kim B-C, Kuchinsky A, Morris JH, et al. Protein network prediction and topological analysis in Leishmania major as a tool for drug target selection. BMC Bioinf. 2010;11(1):484.

    Article  Google Scholar 

  103. Cascante M, Boros LG, Comin-Anduix B, de Atauri P, Centelles JJ, Lee PW-N. Metabolic control analysis in drug discovery and disease. Nat Biotechnol. 2002;20(3):243–9.

    Article  Google Scholar 

  104. Haanstra JR, Gerding A, Dolga AM, Sorgdrager FJH, Buist-Homan M, Du Toit F, et al. Targeting pathogen metabolism without collateral damage to the host. Sci Rep. 2017;7:40406.

    Article  Google Scholar 

  105. Capriles PVSZ, Baptista LPR, Guedes IA, Guimarães ACR, Custódio FL, Alves-Ferreira M, et al. Structural modeling and docking studies of ribose 5-phosphate isomerase from Leishmania major and Homo sapiens: a comparative analysis for leishmaniasis treatment. J Mol Graph Model. 2015;55:134–47.

    Article  Google Scholar 

  106. Otto TD, Guimarães ACR, Degrave WM, de Miranda AB. AnEnPi: identification and annotation of analogous enzymes. BMC Bioinf. 2008;9:544.

    Article  Google Scholar 

  107. Piergiorge RM, de Miranda AB, Guimarães AC, Catanho M. Functional analogy in human metabolism: enzymes with different biological roles or functional redundancy? Genome Biol Evol. 2017;9(6):1624–36.

    Article  Google Scholar 

  108. Mondal SI, Ferdous S, Jewel NA, Akter A, Mahmud Z, Islam MM, et al. Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157: H7: an in silico approach. Adv Appl Bioinf Chem AABC. 2015;8:49.

    Google Scholar 

  109. Su L, Zhou R, Liu C, Wen B, Xiao K, Kong W, et al. Urinary proteomics analysis for sepsis biomarkers with iTRAQ labeling and two-dimensional liquid chromatography–tandem mass spectrometry. J Trauma Acute Care Surg. 2013;74(3):940–5.

    Article  Google Scholar 

  110. Villar M, Ayllón N, Alberdi P, Moreno A, Moreno M, Tobes R, et al. Integrated metabolomics, transcriptomics and proteomics identifies metabolic pathways affected by Anaplasma phagocytophilum infection in tick cells. Mol Cell Proteomics. 2015;14(12):3154–72.

    Article  Google Scholar 

  111. Diamond DL, Syder AJ, Jacobs JM, Sorensen CM, Walters K-A, Proll SC, et al. Temporal proteome and lipidome profiles reveal hepatitis C virus-associated reprogramming of hepatocellular metabolism and bioenergetics. PLoS Pathog. 2010;6(1):e1000719.

    Article  Google Scholar 

  112. Salazar GA, Meintjes A, Mazandu GK, Rapanoël HA, Akinola RO, Mulder NJ. A web-based protein interaction network visualizer. BMC Bioinformatics. 2014;15(1):129.

    Article  Google Scholar 

  113. Pujol A, Mosca R, Farrés J, Aloy P. Unveiling the role of network and systems biology in drug discovery. Trends Pharmacol Sci. 2010;31(3):115–23.

    Article  Google Scholar 

  114. Hormozdiari F, Salari R, Bafna V, Sahinalp SC. Protein-protein interaction network evaluation for identifying potential drug targets. J Comput Biol. 2010;17(5):669–84.

    Article  MathSciNet  Google Scholar 

  115. Holme P, Kim BJ, Yoon CN, Han SK. Attack vulnerability of complex networks. Phys Rev E. 2002;65(5):56109.

    Article  Google Scholar 

  116. Estrada E. Protein bipartivity and essentiality in the yeast protein−protein interaction network. J Proteome Res. 2006;5(9):2177–84.

    Article  Google Scholar 

  117. Kell DB. Systems biology, metabolic modelling and metabolomics in drug discovery and development. Drug Discov Today. 2006;11(23):1085–92.

    Article  Google Scholar 

  118. Jeong H, Mason SP, Barabási A-L, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411(6833):41–2.

    Article  Google Scholar 

  119. Lee I, Lehner B, Crombie C, Wong W, Fraser AG, Marcotte EM. A single gene network accurately predicts phenotypic effects of gene perturbation in Caenorhabditis elegans. Nat Genet. 2008;40(2):181–8.

    Article  Google Scholar 

  120. Kaltdorf M, Srivastava M, Gupta SK, Liang C, Binder J, Dietl A-M, et al. Systematic identification of anti-fungal drug targets by a metabolic network approach. Front Mol Biosci. 2016;3:22.

    Article  Google Scholar 

  121. Joyce AR, Palsson BØ. The model organism as a system: integrating ‘omics’ data sets. Nat Rev Mol Cell Biol. 2006;7(3):198–210.

    Article  Google Scholar 

  122. Oberhardt MA, Palsson BØ, Papin JA. Applications of genome-scale metabolic reconstructions. Mol Syst Biol. 2009;5(1):320.

    Google Scholar 

  123. Orth JD, Thiele I, Palsson BØ. What is flux balance analysis? Nat Biotechnol. 2010;28(3):245–8.

    Article  Google Scholar 

  124. Neidhardt FC. Bacterial growth: constant obsession withdN/dt. J Bacteriol. 1999;181(24):7405–8.

    Google Scholar 

  125. Feist AM, Palsson BO. The biomass objective function. Curr Opin Microbiol. 2010;13(3):344–9.

    Article  Google Scholar 

  126. Ibarra RU, Edwards JS, Palsson BO. Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature. 2002;420(6912):186–9.

    Article  Google Scholar 

  127. Joyce AR, Palsson BØ. Predicting gene essentiality using genome-scale in silico models. Microb Gene Essentiality Protoc Bioinf. 2008;416:433–57.

    Article  Google Scholar 

  128. Sylke M. Comprehensive analysis of parasite biology: from metabolism to drug discovery. Vol. 7. Weinheim: Wiley; 2016. 576 p.

    Google Scholar 

  129. Tobalina L, Pey J, Rezola A, Planes FJ. Assessment of FBA based gene essentiality analysis in cancer with a fast context-specific network reconstruction method. PLoS One. 2016;11(5):e0154583.

    Article  Google Scholar 

  130. Koch A, Biedenkopf D, Furch A, Weber L, Rossbach O, Abdellatef E, et al. An RNAi-based control of fusarium graminearum infections through spraying of long dsRNAs involves a plant passage and is controlled by the fungal silencing machinery. PLoS Pathog. 2016;12(10):e1005901.

    Article  Google Scholar 

  131. Peyraud R, Dubiella U, Barbacci A, Genin S, Raffaele S, Roby D. Advances on plant–pathogen interactions from molecular toward systems biology perspectives. Plant J. 2017;90(4):720–37.

    Article  Google Scholar 

  132. Aziz N, Zhao Q, Bry L, Driscoll DK, Funke B, Gibson JS, et al. College of American Pathologists’ laboratory standards for next-generation sequencing clinical tests. Arch Pathol Lab Med. 2014;139(4):481–93.

    Article  Google Scholar 

Download references

Acknowledgment

This study was supported by a fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (http://www.capes.gov.br/) to LCC.

Author information

Authors and Affiliations

  1. Laboratório de Modelagem de Sistemas Biológicos, Centro de Desenvolvimento Tecnológico em Saúde, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil

    Larissa Catharina & Nicolas Carels

  2. Instituto de Física, Universidade Federal Fluminense, Rio de Janeiro, Brazil

    Marcio Argollo de Menezes

Authors
  1. Larissa Catharina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcio Argollo de Menezes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nicolas Carels
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolas Carels .

Editor information

Editors and Affiliations

  1. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Fabricio Alves Barbosa da Silva

  2. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Nicolas Carels

  3. Graduate Program in Computational Biology, Oswaldo Cruz Foundation, Rio de Janeiro, Rio de Janeiro, Brazil

    Floriano Paes Silva Junior

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Catharina, L., de Menezes, M.A., Carels, N. (2018). System Biology to Access Target Relevance in the Research and Development of Molecular Inhibitors. In: Alves Barbosa da Silva, F., Carels, N., Paes Silva Junior, F. (eds) Theoretical and Applied Aspects of Systems Biology. Computational Biology, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-74974-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-74974-7_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74973-0

  • Online ISBN: 978-3-319-74974-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation