Development: Multiscale CSB—Simulation Tools

Chapter
Part of the SpringerBriefs in Pharmaceutical Science & Drug Development book series (BRIEFSPSDD, volume 2)

Abstract

In order to cover bottom-up and top-down phenomena multiscale SB simulation tools should include organ-level considerations, and should be used in conjunction with multiscale modeling tools which have the ability to handle many orders of magnitude in both length and timescale. Several new R&D paradigms, based on CSB, are proposed, while some are already in the research stage. This effort will lead to virtual organ/disease models, emerging as important tools. Identifying and targeting a system’s emergent properties is a major goal for coming years. This will cause a paradigm shift in R&D activity in Pharma yielding a move from population models to models of individualized medicine. The importance of multiscale CSB is underlined here as a great attention is given here in this section.

Keywords

Cellular Automaton Parkinson Disease Emergent Property Multiscale Model Correlation Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Prokop A (1982) Systems analysis and synthesis in biology and biotechnology. Internat J General Systems 8:1–25CrossRefGoogle Scholar
  2. 2.
    Prokop A, Bajpai RK (1990) Bioreactor design and operation, In: progress in recombinant DNA technology and applications, Prokop A, Bajpai RK, Ho CS (eds) McGraw-Hill, New York, pp. 415–459Google Scholar
  3. 3.
    Coveney PV, Fowler PW (2005) Modelling biological complexity: a physical scientist’s perspective. J R Soc Interface 2:267–280PubMedCrossRefGoogle Scholar
  4. 4.
    Anderson AR, Weaver AM, Cummings PT, Quaranta V (2006) Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell 127(5):905–915PubMedCrossRefGoogle Scholar
  5. 5.
    Anderson AR, Rejniak KA, Gerlee P, Quaranta V (2008) Microenvironment driven invasion: a multiscale multimodel investigation. J Math Biol 58(4–5):579–624PubMedGoogle Scholar
  6. 6.
    Smith AE, Slepchenko BM, Schaff JC, Loew LM, Macara IG (2002) Systems analysis of Ran transport. Science 295(5554):488–491PubMedCrossRefGoogle Scholar
  7. 7.
    Auffray C, Nottale L (2008) Scale relativity theory and integrative systems biology: 1. Founding principles and scale laws. Prog Biophys Mol Biol 97(1):79–114PubMedCrossRefGoogle Scholar
  8. 8.
    Chavali AK, Gianchandani EP, Tung KS, Lawrence MB, Peirce SM, Papin JA (2008) Characterizing emergent properties of immunological systems with multi-cellular rule-based computational modeling. Trends Immunol 29(12):589–599PubMedCrossRefGoogle Scholar
  9. 9.
    Zhang L, Wang Z, Sagotsky JA, Deisboeck TS (2009) Multiscale agent-based cancer modeling. J Math Biol 58(4–5):545–559PubMedCrossRefGoogle Scholar
  10. 10.
    McCarty J, Lyubimov IY, Guenza MG (2009) Multiscale modeling of coarse-grained macromolecular liquids. J Phys Chem B 113(35):11876–11886PubMedCrossRefGoogle Scholar
  11. 11.
    Meier-Schellersheim M, Fraser IDC, Klauschen F (2009) Multiscale modeling for biologists. WIRE Syst Biol Med 1:4–14CrossRefGoogle Scholar
  12. 12.
    Evans DJ, Lawford PV, Gunn J, Walker D, Hose DR, Smallwood RH, Chopard B, Krafczyk M, Bernsdorf J, Hoekstra A (2008) The application of multiscale modelling to the process of development and prevention of stenosis in a stented coronary artery. Philos Transact A Math Phys Eng Sci 366(1879):3343–3360PubMedCrossRefGoogle Scholar
  13. 13.
    Prokop A, Davidson JM (2008) Nanovehicular intracellular delivery systems. J Pharm Sci 97(9):3518–3590PubMedCrossRefGoogle Scholar
  14. 14.
    Quaranta V, Rejniak KA, Gerlee P, Anderson AR (2008) Invasion emerges from cancer cell adaptation to competitive microenvironments: quantitative predictions from multiscale mathematical models. Semin Cancer Biol 18(5):338–348PubMedCrossRefGoogle Scholar
  15. 15.
    Robertson SH, Smith CK, Langhans AL, McLinden SE, Oberhardt MA, Jakab KR, Dzamba B, DeSimone DW, Papin JA, Peirce SM (2007) Multiscale computational analysis of Xenopus laevis morphogenesis reveals key insights of systems-level behavior. BMC Syst Biol 1:46PubMedCrossRefGoogle Scholar
  16. 16.
    Breakspear M, Stam CJ (2005) Dynamics of a neural system with a multiscale architecture. Philos Trans R Soc Lond B Biol Sci 360(1457):1051–1074PubMedCrossRefGoogle Scholar
  17. 17.
    Rullmann JA, Struemper H, Defranoux NA, Ramanujan S, Meeuwisse CM, van Elsas A (2005) Systems biology for battling rheumatoid arthritis: application of the Entelos PhysioLab platform. Syst Biol (Stevenage) 152(4):256–262Google Scholar
  18. 18.
    Maxwell G, Mackay C (2008) Application of a systems biology approach to skin allergy risk assessment. Altern Lab Anim 36(5):521–556PubMedGoogle Scholar
  19. 19.
    Gadkar KG, Shoda LK, Kreuwel HT, Ramanujan S, Zheng Y, Whiting CC, Young DL (2007) Dosing and timing effects of anti-CD40L therapy: predictions from a mathematical model of type 1 diabetes. Ann N Y Acad Sci 1103:63–68PubMedCrossRefGoogle Scholar
  20. 20.
    Shoda L, Kreuwel H, Gadkar K, Zheng Y, Whiting C, Atkinson M, Bluestone J, Mathis D, Young D, Ramanujan S (2010) The Type 1 Diabetes PhysioLab Platform: a validated physiologically based mathematical model of pathogenesis in the non-obese diabetic mouse. Clin Exp Immunol 161(2):250–267PubMedGoogle Scholar
  21. 21.
    Whiting CC (2007) The virtual NOD mouse: applying predictive biosimulation to research in type 1 diabetes. Ann N Y Acad Sci 1103:45–62PubMedCrossRefGoogle Scholar
  22. 22.
    Zheng Y, Kreuwel HT, Young DL, Shoda LK, Ramanujan S, Gadkar KG, Atkinson MA, Whiting CC (2007) The virtual NOD mouse: applying predictive biosimulation to research in type 1 diabetes. Ann N Y Acad Sci 1103:45–62PubMedCrossRefGoogle Scholar
  23. 23.
    Bharath MM (2008) Insights into the effects of alpha-synuclein expression and proteasome inhibition on glutathione metabolism through a dynamic in silico model of Parkinson’s disease: validation by cell culture data. Free Radic Biol Med 45(9):1290–1301PubMedCrossRefGoogle Scholar
  24. 24.
    Vali S, Chinta SJ, Peng J, Sultana Z, Singh N, Sharma P, Sharada S, Andersen JK, Bharath MM (2008) Insights into the effects of alpha-synuclein expression and proteasome inhibition on glutathione metabolism through a dynamic in silico model of Parkinson’s disease: validation by cell culture data. Free Radic Biol Med 45(9):1290–1301PubMedCrossRefGoogle Scholar
  25. 25.
    Vodovotz Y, Chow CC, Bartels J, Lagoa C, Prince JM, Levy RM, Kumar R, Day J, Rubin J, Constantine G, Billiar TR, Fink MP, Clermont G (2006) In silico models of acute inflammation in animals. Shock 26(3):235–244PubMedCrossRefGoogle Scholar
  26. 26.
    Kronik N, Kogan Y, Elishmereni M, Halevi-Tobias K, Vuk-Pavlović S, Agur Z (2010) Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models. PLoS One 5(12):e15482PubMedCrossRefGoogle Scholar
  27. 27.
    Agur Z (2006) Biomathematics in the development of personalized medicine in oncology. Future Oncol 2(1):39–42PubMedCrossRefGoogle Scholar
  28. 28.
    Nicholson JK (2006) Global systems biology, personalized medicine and molecular epidemiology. Mol Syst Biol 2:52PubMedCrossRefGoogle Scholar
  29. 29.
    Stout NK, Goldie SJ (2008) Keeping the noise down: common random numbers for disease simulation modeling. Health Care Manag Sci 11(4):399–406PubMedCrossRefGoogle Scholar
  30. 30.
    Barendregt JJ, Van Oortmarssen GJ, Vos T, Murray CJ (2003) A generic model for the assessment of disease epidemiology: the computational basis of DisMod II. Popul Health Metr 1(1):4PubMedCrossRefGoogle Scholar
  31. 31.
    Fattore M, Arrigo P (2005) Knowledge discovery and system biology in molecular medicine: an application on neurodegenerative diseases. In Silico Biol 5(2):199–208PubMedGoogle Scholar
  32. 32.
    Palacios R, Goni J, Martinez-Forero I, Iranzo J, Sepulcre J, Melero I, Villoslada P (2007) A network analysis of the human T-cell activation gene network identifies JAGGED1 as a therapeutic target for autoimmune diseases. PLoS ONE 2(11):e1222PubMedCrossRefGoogle Scholar
  33. 33.
    Qu K, Abi Haidar A, Fan J, Ensman L, Tuncay K, Jolly M, Ortoleva P (2007) Cancer onset and progression: a genome-wide, nonlinear dynamical systems perspective on onconetworks. J Theor Biol 246(2):234–244PubMedCrossRefGoogle Scholar
  34. 34.
    De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Microbiol 8(10):717–729PubMedGoogle Scholar
  35. 35.
    Sieberts SK, Schadt EE (2007) Moving toward a system genetics view of disease. Mamm Genome 18:389–401PubMedCrossRefGoogle Scholar
  36. 36.
    Butcher EC (2005) Can cell systems biology rescue drug discovery? Nat Rev Drug Discov 4(6):461–467PubMedCrossRefGoogle Scholar
  37. 37.
    van der Greef J, McBurney RN (2005) Innovation: Rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat Rev Drug Discov 4(12):961–967Google Scholar
  38. 38.
    Lindsay MA (2003) Target discovery. Nat Rev Drug Discov 2(10):831–838PubMedCrossRefGoogle Scholar
  39. 39.
    Hecker M, Lambeck S, Toepfer S, Van Someren E, Guthke R (2009) Gege regulatory network inference: ata integration in dynamic models–a review. Biosystems 96(1):86–103CrossRefGoogle Scholar
  40. 40.
    Pujana MA, Han JD, Starita LM, Stevens KN, Tewari M, Ahn JS, Rennert G, Moreno V, Kirchhoff T, Gold B, Assmann V, Elshamy WM, Rual JF, Levine D, Rozek LS, Gelman RS, Gunsalus KC, Greenberg RA, Sobhian B, Bertin N, Venkatesan K, Ayivi-Guedehoussou N, Solé X, Hernández P, Lázaro C, Nathanson KL, Weber BL, Cusick ME, Hill DE, Offit K, Livingston DM, Gruber SB, Parvin JD, Vidal M (2007) Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat Genet 39(11):1338–1349PubMedCrossRefGoogle Scholar
  41. 41.
    Ergün A, Lawrence CA, Kohanski MA, Brennan TA, Collins JJ (2007) A network biology approach to prostate cancer. Mol Syst Biol 3:82PubMedCrossRefGoogle Scholar
  42. 42.
    Lee Y, Yang X, Huang Y, Fan H, Zhang Q, Wu Y, Li J, Hasina R, Cheng C, Lingen MW, Gerstein MB, Weichselbaum RR, Xing HR, Lussier YA (2010) Network modeling identifies molecular functions targeted by miR-204 to suppress head and neck tumor metastasis. PLoS Comput Biol. 6(4):e1000730PubMedCrossRefGoogle Scholar
  43. 43.
    Zhao S, Li S (2010) Network-based relating pharmacological and genomic spaces for drug target identification. PLoS One 5(7):e11764PubMedCrossRefGoogle Scholar
  44. 44.
    Klipp E, Wade RC, Kummer U (2010) Biochemical network-based drug-target prediction. Curr Opin Biotechnol 21(4):511–516PubMedCrossRefGoogle Scholar
  45. 45.
    Hendricks BS, Griffiths GJ, Benson R, Kenyon D, Lazzara M, Swinton J, Beck S, Hickinson M, Beusmans JM, Lauffenburger D, de Graaf D (2006) Decreased internalisation of erbB1 mutants in lung cancer is linked with a mechanism conferring sensitivity to gefitinib. Syst Biol (Stevenage) 153(6):457–466Google Scholar
  46. 46.
    Xu JJ, Hendriks BS, Zhao J, de Graaf D (2008) Multiple effects of acetaminophen and p38 inhibitors: towards pathway toxicology. FEBS Lett 582(8):1276–1282PubMedCrossRefGoogle Scholar
  47. 47.
    Khalil IG, Hill C (2005) Systems biology for cancer. Curr Opin Oncol 17(1):44–48PubMedCrossRefGoogle Scholar
  48. 48.
    Skogsberg J, Lundström J, Kovacs A, Nilsson R, Noori P, Maleki S, Köhler M, Hamsten A, Tegnér J, Björkegren J (2008) Transcriptional profiling uncovers a network of cholesterol-responsive atherosclerosis target genes. PLoS Genet 4(3):e1000036PubMedCrossRefGoogle Scholar
  49. 49.
    Southern J, Pitt-Francis J, Whiteley J, Stokeley D, Kobashi H, Nobes R, Kadooka Y, Gavaghan D (2008) Multi-scale computational modelling in biology and physiology. Prog Biophys Mol Biol 96(1–3):60–89PubMedCrossRefGoogle Scholar
  50. 50.
    Liu JP, Lin JR (2008) Statistical methods for targeted clinical trials under enrichment design. J Formos Med Assoc 107(Suppl 12):35–42PubMedCrossRefGoogle Scholar

Copyright information

© The Author(s) 2012

Authors and Affiliations

  1. 1.Chemical and Biomolecular EngineeringVanderbilt UniversityNashvilleUSA
  2. 2.NanoDelivery International, s.r.o.Břeclav-PoštornáCzech Republic
  3. 3.Genomic Health IncRedwood CityUSA

Personalised recommendations