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Computational and Mathematical Methods in Medicine
Volume 2012, Article ID 676015, 9 pages
http://dx.doi.org/10.1155/2012/676015
Review Article

Understanding Immunology via Engineering Design: The Role of Mathematical Prototyping

1Department of Chemical Engineering and Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 25606, USA
2Department of Microbiology, Immunology and Cell Biology, West Virginia University, Morgantown, WV 25606, USA
3Department of Computer Sciences, Mathematics, and Engineering, Shepherd University, Shepherdstown, WV 25433, USA

Received 15 June 2012; Accepted 2 August 2012

Academic Editor: Francesco Pappalardo

Copyright © 2012 David J. Klinke and Qing Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. K. Abbas and C. A. Janeway Jr., “Immunology: improving on nature in the twenty-first century,” Cell, vol. 100, no. 1, pp. 129–138, 2000. View at Google Scholar · View at Scopus
  2. FDA, “Innovation or stagnation: challenge and opportunity on the critical path to new medical products,” 2004.
  3. D. B. Searls, “Data integration: challenges for drug discovery,” Nature Reviews Drug Discovery, vol. 4, no. 1, pp. 45–58, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. P. K. Sorger and S. R. B. Allerheiligen, “Quantitative and systems pharmacology in the postgenomic era: new approaches to discovering drugs and understanding therapeutic mechanisms,” Tech. Rep., 2011, An NIH white paper by the QSP workshop group, NIGMS. View at Google Scholar
  5. The Congress of the United States and Congressional Budget Office, Research and development in the pharmaceutical industry, 2006.
  6. J. M. Ottino, “New tools, new outlooks, new opportunities,” AIChE Journal, vol. 51, no. 7, pp. 1840–1845, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. National Research Council (U.S.) Committee on Challenges for the Chemical Sciences in the 21st Century, Beyond the Molecular Frontier: Challenges for Chemistry and Chemical Engineering, National Academies Press, Washington, DC, USA, 2003.
  8. G. Stephanopoulos, “Chemical and biological engineering,” Chemical Engineering Science, vol. 58, pp. 3291–3293, 2003. View at Google Scholar
  9. National Research Council (U.S.). Committee on Defining and Advancing the Conceptual Basis of Biology in the 21st Century, The Role of Theory in Advancing 21st Century Biology: Catalyzing Transformative Research, National Academies Press, Washington, DC, USA, 2008.
  10. J. Keener and J. Sneyd, Mathematical Physiology, Springer, New York, NY, USA, 2001.
  11. L. S. Hirsch, S. J. Gibbons, H. Kimmel, R. Rockland, and J. Bloom, “High school students—attitudes to and knowledge about engineering,” Frontiers in Education, FIE, 33rd Annual, 2:2, 2003.
  12. W. G. Vincenti, What Engineers Know and How They Know It, John Hopkins Press, Baltimore, Md, USA, 1990.
  13. P. G. Dominick, J. T. Demel, W. M. Lawbaugh, R. J. Freuler, G. L. Kinzel, and E. Fromm, Tools and Tactics of Design, John Wiley & Sons, New York, NY, USA, 2001.
  14. J. M. Juran, Juran on Leadership for Quality, Free Press, New York, NY, USA, 1989.
  15. R. Aris, Mathematical Modelling Techniques, Dover Publications, Mineola, NY, USA, 1995.
  16. A. Saltelli, K. Chan, and E. M. Scott, Sensitivity Analysis Wiley Series in Probability and Statistics, John Wiley & Sons, New York, NY, USA, 2000.
  17. D. J. Klinke, “An empirical Bayesian approach for model-based inference of cellular signaling networks,” BMC Bioinformatics, vol. 10, no. 1, article 371, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. S. L. Star and J. R. Griesemer, “Institutional ecology, translations and boundary objects—amateurs and professionals in berkeleys museum of vertebrate zoology,” Social Studies of Science, vol. 19, pp. 387–420, 1989. View at Google Scholar
  19. National Research Council (U.S.). Committee on Learning, How People Learn: Brain, Mind, Experience, and School, National Academies Press, Washington, DC, USA, 2000.
  20. World Health Organization, “Report of a WHO consultation,” Part 1: diagnosis and classification of diabetes mellitus, 1999.
  21. M. A. Atkinson and G. S. Eisenbarth, “Type 1 diabetes: new perspectives on disease pathogenesis and treatment,” The Lancet, vol. 358, no. 9277, pp. 221–229, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. P. Onkamo, S. Väänänen, M. Karvonen, and J. Tuomilehto, “Worldwide increase in incidence of type I diabetes—the analysis of the data on published incidence trends,” Diabetologia, vol. 42, no. 12, pp. 1395–1403, 1999. View at Publisher · View at Google Scholar · View at Scopus
  23. E. A. M. Gale, “Can we change the course of beta-cell destruction in type 1 diabetes?” The New England Journal of Medicine, vol. 346, no. 22, pp. 1740–1742, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. J. Daaboul and D. Schatz, “Overview of prevention and intervention trials for type 1 diabetes,” Reviews in Endocrine and Metabolic Disorders, vol. 4, no. 4, pp. 317–323, 2003. View at Publisher · View at Google Scholar · View at Scopus
  25. A. K. Foulis, C. N. Liddle, M. A. Farquharson, J. A. Richmond, and R. S. Weir, “The histopathology of the pancreas in type I (insulin-dependent) diabetes mellitus: a 25-year review of deaths in patients under 20 years of age in the United Kingdom,” Diabetologia, vol. 29, no. 5, pp. 267–274, 1986. View at Google Scholar · View at Scopus
  26. A. K. Foulis, M. A. Farquharson, and R. Hardman, “Aberrant expression of class II major histocompatibility complex molecules by B cells and hyperexpression of Class I major histocompatibility complex molecules by insulin containing islets in Type 1 (insulin-dependent) diabetes mellitus,” Diabetologia, vol. 30, no. 5, pp. 333–343, 1987. View at Google Scholar · View at Scopus
  27. W. Gepts, “Pathologic anatomy of the pancreas in juvenile diabetes mellitus,” Diabetes, vol. 14, no. 10, pp. 619–633, 1965. View at Google Scholar · View at Scopus
  28. N. A. Sherry, E. B. Tsai, and K. C. Herold, “Natural history of β-cell function in type 1 diabetes,” Diabetes, vol. 54, supplement 2, pp. S32–S39, 2005. View at Publisher · View at Google Scholar · View at Scopus
  29. D. J. Klinke, “Extent of beta cell destruction is important but insufficient to predict the onset of type 1 diabetes mellitus,” PLoS ONE, vol. 3, no. 1, Article ID e1374, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. J. Komulainen, M. Knip, R. Lounamaa et al., “Poor beta-cell function after the clinical manifestation of type 1 diabetes in children initially positive for islet cell specific autoantibodies,” Diabetic Medicine, vol. 14, pp. 532–537, 1997. View at Google Scholar
  31. P. Pozzilli, D. Pitocco, N. Visalli et al., “No effect of oral insulin on residual beta-cell function in recent-onset type I diabetes (the IMDIAB VII),” Diabetologia, vol. 43, no. 8, pp. 1000–1004, 2000. View at Publisher · View at Google Scholar · View at Scopus
  32. L. Chaillous, H. Lefèvre, C. Thivolet et al., “Oral insulin administration and residual β-cell function in recent-onset type 1 diabetes: a multicentre randomised controlled trial,” The Lancet, vol. 356, no. 9229, pp. 545–549, 2000. View at Google Scholar · View at Scopus
  33. R. J. Kuczmarski, C. L. Ogden, L. M. Grummer-Strawn et al., “CDC growth charts: United States,” Advance Data, no. 314, pp. 1–27, 2000. View at Google Scholar · View at Scopus
  34. D. J. Klinke, “Age-corrected beta cell mass following onset of type 1 diabetes mellitus correlates with plasma C-peptide in humans,” PLoS ONE, vol. 6, no. 11, Article ID e26873, 2011. View at Google Scholar
  35. R. N. Germain, “The art of the probable: system control in the adaptive immune system,” Science, vol. 293, no. 5528, pp. 240–245, 2001. View at Publisher · View at Google Scholar · View at Scopus
  36. A. O'Garra, L. Gabryšová, and H. Spits, “Quantitative events determine the differentiation and function of helper T cells,” Nature Immunology, vol. 12, no. 4, pp. 288–294, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. H. Ueno, E. Klechevsky, R. Morita et al., “Dendritic cell subsets in health and disease,” Immunological Reviews, vol. 219, no. 1, pp. 118–142, 2007. View at Publisher · View at Google Scholar · View at Scopus
  38. R. M. Steinman, “Decisions about dendritic cells: past, present, and future,” Annual Review of Immunology, vol. 30, pp. 1–22, 2012. View at Google Scholar
  39. J. Banchereau, F. Briere, C. Caux et al., “Immunobiology of dendritic cells,” Annual Review of Immunology, vol. 18, pp. 767–811, 2000. View at Publisher · View at Google Scholar · View at Scopus
  40. F. L. Jahnsen, E. D. Moloney, T. Hogan, J. W. Upham, C. M. Burke, and P. G. Holt, “Rapid dendritic cell recruitment to the bronchial mucosa of patients with atopic asthma in response to local allergen challenge,” Thorax, vol. 56, no. 11, pp. 823–826, 2001. View at Publisher · View at Google Scholar · View at Scopus
  41. D. J. Klinke, “An age-structured model of dendritic cell trafficking in the lung,” American Journal of Physiology, vol. 291, no. 5, pp. L1038–L1049, 2006. View at Publisher · View at Google Scholar · View at Scopus
  42. D. J. Klinke, “A multi-scale model of dendritic cell education and trafficking in the lung: implications for T cell polarization,” Annals of Biomedical Engineering, vol. 35, no. 6, pp. 937–955, 2007. View at Publisher · View at Google Scholar · View at Scopus
  43. A. G. Fredrickson, “Population balance equations for cell and microbial cultures revisited,” AIChE Journal, vol. 49, no. 4, pp. 1050–1059, 2003. View at Publisher · View at Google Scholar · View at Scopus
  44. J. Bélair, M. C. Mackey, and J. M. Mahaffy, “Age-structured and two-delay models for erythropoiesis,” Mathematical Biosciences, vol. 128, no. 1-2, pp. 317–346, 1995. View at Publisher · View at Google Scholar · View at Scopus
  45. J. M. Mahaffy, J. Bélair, and M. C. Mackey, “Hematopoietic model with moving boundary condition and state dependent delay: applications in erythropoiesis,” Journal of Theoretical Biology, vol. 190, no. 2, pp. 135–146, 1998. View at Publisher · View at Google Scholar · View at Scopus
  46. R. H. Schwartz and D. L. Mueller, “Immunological tolerance,” in Fundamental Immunology, W. E. Paul, Ed., Lippincott Williams & Wilkins, Philadelphia, Pa, USA, 2003. View at Google Scholar
  47. S. E. Henrickson, T. R. Mempel, I. B. Mazo et al., “T cell sensing of antigen dose governs interactive behavior with dendritic cells and sets a threshold for T cell activation,” Nature Immunology, vol. 9, no. 3, pp. 282–291, 2008. View at Publisher · View at Google Scholar · View at Scopus
  48. American Association for the Advancement of Science, Science for All Americans, Oxford University Press, New York, NY, USA, 1990.
  49. H. Akaike, “A new look at the statistical model identification,” IEEE Transactions on Automatic Control, vol. 19, no. 6, pp. 716–723, 1974. View at Google Scholar · View at Scopus
  50. K. Yamaoka, T. Nakagawa, and T. Uno, “Application of Akaike's information criterion (AIC) in the evaluation of linear pharmacokinetics equations,” Journal of Pharmacokinetics and Biopharmaceutics, vol. 6, no. 2, pp. 165–175, 1978. View at Google Scholar · View at Scopus
  51. R. Horn, “Statistical methods for model discrimination. Applications to gating kinetics and permeation of the acetylcholine receptor channel,” Biophysical Journal, vol. 51, no. 2, pp. 255–263, 1987. View at Google Scholar · View at Scopus
  52. B. Efron and R. Tibshirani, “Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy,” Statistical Science, vol. 1, pp. 54–75, 1986. View at Google Scholar
  53. M. R. Chernick, Bootstrap Methods: A Practitioner's Guide, John Wiley & Sons, New York, NY, USA, 1999.
  54. M. G. Pedersen, R. Bertram, and A. Sherman, “Intra- and inter-islet synchronization of metabolically driven insulin secretion,” Biophysical Journal, vol. 89, no. 1, pp. 107–119, 2005. View at Publisher · View at Google Scholar · View at Scopus
  55. R. Nesher and E. Cerasi, “Modeling phasic insulin release: immediate and time-dependent effects of glucose,” Diabetes, vol. 51, supplement 1, pp. S53–S59, 2002. View at Google Scholar · View at Scopus
  56. A. E. Butler, J. Janson, S. Bonner-Weir, R. Ritzel, R. A. Rizza, and P. C. Butler, “β-cell deficit and increased β-cell apoptosis in humans with type 2 diabetes,” Diabetes, vol. 52, no. 1, pp. 102–110, 2003. View at Publisher · View at Google Scholar · View at Scopus
  57. A. R. Sedaghat, A. Sherman, and M. J. Quon, “A mathematical model of metabolic insulin signaling pathways,” American Journal of Physiology, vol. 283, no. 5, pp. E1084–E1101, 2002. View at Google Scholar · View at Scopus
  58. L. Shoda, H. Kreuwel, K. Gadkar et al., “The type 1 diabetes physioLab platform: a validated physiologically based mathematical model of pathogenesis in the non-obese diabetic mouse,” Clinical and Experimental Immunology, vol. 161, no. 2, pp. 250–267, 2010. View at Publisher · View at Google Scholar · View at Scopus
  59. S. Marino and D. E. Kirschner, “The human immune response to Mycobacterium tuberculosis in lung and lymph node,” Journal of Theoretical Biology, vol. 227, no. 4, pp. 463–486, 2004. View at Publisher · View at Google Scholar · View at Scopus
  60. V. E. Woodhead, M. H. Binks, B. M. Chain, and D. R. Katz, “From sentinel to messenger: an extended phenotypic analysis of the monocyte to dendritic cell transition,” Immunology, vol. 94, no. 4, pp. 552–559, 1998. View at Publisher · View at Google Scholar · View at Scopus
  61. A. Dzionek, A. Fuchs, P. Schmidt et al., “BDCA-2, BDCA-3, and BDCA-4: three markers for distinct subsets of dendritic cells in human peripheral blood,” Journal of Immunology, vol. 165, no. 11, pp. 6037–6046, 2000. View at Google Scholar · View at Scopus
  62. C. Caux, C. Massacrier, B. Vanbervliet et al., “CD34+ hematopoietic progenitors from human cord blood differentiate along two independent dendritic cell pathways in response to granulocyte- macrophage colony-stimulating factor plus tumor necrosis factor α: II. Functional analysis,” Blood, vol. 90, no. 4, pp. 1458–1470, 1997. View at Google Scholar · View at Scopus
  63. J. A. Jacquez and T. Perry, “Parameter estimation: local identifiability of parameters,” American Journal of Physiology, vol. 258, no. 4, pp. E727–E736, 1990. View at Google Scholar · View at Scopus
  64. S. Audoly, G. Bellu, L. D'Angiò, M. P. Saccomani, and C. Cobelli, “Global identifiability of nonlinear models of biological systems,” IEEE Transactions on Biomedical Engineering, vol. 48, no. 1, pp. 55–65, 2001. View at Publisher · View at Google Scholar · View at Scopus
  65. J. Hu, G. Nudelman, Y. Shimoni et al., “Role of cell-to-cell variability in activating a positive feedback antiviral response in human dendritic cells,” PLoS ONE, vol. 6, no. 2, Article ID e16614, 2011. View at Publisher · View at Google Scholar · View at Scopus
  66. J. J. Linderman, T. Riggs, M. Pande, M. Miller, S. Marino, and D. E. Kirschner, “Characterizing the dynamics of CD4+ T cell priming within a lymph node,” Journal of Immunology, vol. 184, no. 6, pp. 2873–2885, 2010. View at Publisher · View at Google Scholar · View at Scopus
  67. G. Bogle and P. R. Dunbar, “Agent-based simulation of T-cell activation and proliferation within a lymph node,” Immunology and Cell Biology, vol. 88, no. 2, pp. 172–179, 2010. View at Publisher · View at Google Scholar · View at Scopus
  68. V. Baldazzi, P. Paci, M. Bernaschi, and F. Castiglione, “Modeling lymphocyte homing and encounters in lymph nodes,” BMC Bioinformatics, vol. 10, article 387, 2009. View at Publisher · View at Google Scholar · View at Scopus
  69. S. D. Hester, J. M. Belmonte, J. S. Gens, S. G. Clendenon, and J. A. Glazier, “A multicell, multi-scale model of vertebrate segmentation and somite formation,” PLoS Computational Biology, vol. 7, no. 10, Article ID e1002155, 2011. View at Google Scholar
  70. A. R. A. Anderson and V. Quaranta, “Integrative mathematical oncology,” Nature Reviews Cancer, vol. 8, no. 3, pp. 227–234, 2008. View at Publisher · View at Google Scholar · View at Scopus
  71. C. T. H. Baker, G. A. Bocharov, J. M. Ford et al., “Computational approaches to parameter estimation and model selection in immunology,” Journal of Computational and Applied Mathematics, vol. 184, no. 1, pp. 50–76, 2005. View at Publisher · View at Google Scholar · View at Scopus
  72. S. M. Andrew, C. T. H. Baker, and G. A. Bocharov, “Rival approaches to mathematical modelling in immunology,” Journal of Computational and Applied Mathematics, vol. 205, no. 2, pp. 669–686, 2007. View at Publisher · View at Google Scholar · View at Scopus
  73. J. R. Faeder, M. L. Blinov, and W. S. Hlavacek, “Rule-based modeling of biochemical systems with BioNetGen,” Methods in Molecular Biology, vol. 500, pp. 113–167, 2009. View at Google Scholar · View at Scopus
  74. J. Feret, V. Danos, J. Krivine, R. Harmer, and W. Fontana, “Internal coarse-graining of molecular systems,” Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 16, pp. 6453–6458, 2009. View at Publisher · View at Google Scholar · View at Scopus
  75. J. A. Bachman and P. Sorger, “New approaches to modeling complex biochemistry,” Nature Methods, vol. 8, no. 2, pp. 130–131, 2011. View at Publisher · View at Google Scholar · View at Scopus
  76. D. J. Klinke and S. D. Finley, “Timescale analysis of rule-based biochemical reaction networks,” Biotechnology Progress, vol. 28, pp. 33–44, 2012. View at Google Scholar
  77. S. D. Finley, D. Gupta, N. Cheng, and D. J. Klinke, “Inferring relevant control mechanisms for interleukin-12 signaling in nave CD4 T cells,” Immunology and Cell Biology, vol. 89, no. 1, pp. 100–110, 2011. View at Publisher · View at Google Scholar · View at Scopus
  78. D. J. Klinke, N. Cheng, and E. Chambers, “Quantifying cross-talk among interferongamma, interleukin-12 and tumor necrosis factor signaling pathways within a Th1 cell model,” Science Signaling, vol. 5, no. 220, article ra32, 2012. View at Google Scholar