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Systems Medicine pp 107–118Cite as

Systems Medicine and Infection

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1386))

Abstract

By using a systems-based approach, mathematical and computational techniques can be used to develop models that describe the important mechanisms involved in infectious diseases. An iterative approach to model development allows new discoveries to continually improve the model and ultimately increase the accuracy of predictions.

SIR models are used to describe epidemics, predicting the extent and spread of disease. Genome-wide genotyping and sequencing technologies can be used to identify the biological mechanisms behind diseases. These tools help to build strategies for disease prevention and treatment, an example being the recent outbreak of Ebola in West Africa where these techniques were deployed.

HIV is a complex disease where much is still to be learned about the virus and the best effective treatment. With basic mathematical modeling techniques, significant discoveries have been made over the last 20 years. With recent technological advances, the computational resources now available, and interdisciplinary cooperation, further breakthroughs are inevitable.

In TB, modeling has traditionally been empirical in nature, with clinical data providing the fuel for this top-down approach. Recently, projects have begun to use data derived from laboratory experiments and clinical trials to create mathematical models that describe the mechanisms responsible for the disease.

A systems medicine approach to infection modeling helps identify important biological questions that then direct future experiments, the results of which improve the model in an iterative cycle. This means that data from several model systems can be integrated and synthesized to explore complex biological systems.

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References

  1. Murray JD (2002) Mathematical biology. Springer, New York

    Google Scholar 

  2. Kermack WO, McKendrick AG (1932) Contributions to the mathematical theory of epidemics. II. The Problem of endemicity. Proc R Soc London A 138:55–83. doi:10.1098/rspa.1932.0171

    Google Scholar 

  3. Anderson RM, May RM (1991) Infectious diseases of humans: dynamics and control. Oxford University Press, Oxford, p 757

    Google Scholar 

  4. Diekmann O, Heesterbeek J (2000) Mathematical epidemiology of infectious diseases: model building, analysis and interpretation. Wiley, Chichester, p 303

    Google Scholar 

  5. Britton T (2010) Stochastic epidemic models: a survey. Math Biosci 225:24–35. doi:10.1016/j.mbs.2010.01.006

    Article  PubMed  Google Scholar 

  6. Bailey NT (1987) The mathematical theory of infectious diseases. Macmillan Publishing Company, New York

    Google Scholar 

  7. Becker NG (1989) Analysis of infectious disease data, monographs on statistics and applied probability. Chapman & Hall, London, UK

    Google Scholar 

  8. Ho DD, Neumann AU, Perelson AS, Chen W (1995) Rapid turnover of plasma virions and CD4 lymphocytes in HIV-1 infection. Nature 373(6510):123–126

    Article  CAS  PubMed  Google Scholar 

  9. Perelson AS, Nelson PW (1999) Mathematical analysis of HIV-1 dynamics in vivo. SIAM Rev 41:3–44. doi:10.1137/S0036144598335107

    Article  Google Scholar 

  10. Wei X, Ghosh SK, Taylor ME et al (1995) Viral dynamics in human immunodeficiency virus type 1 infection. Nature 373(6510):117–122

    Article  CAS  PubMed  Google Scholar 

  11. Perelson AS, Essunger P, Cao Y et al (1997) Decay characteristics of HIV-1-infected compartments during combination therapy. Nature 387:188–191. doi:10.1038/387188a0

    Article  CAS  PubMed  Google Scholar 

  12. Mitchison DA (2005) The diagnosis and therapy of tuberculosis during the past 100 years. Am J Respir Crit Care Med 171:699–706. doi:10.1164/rccm.200411-1603OE

    Article  PubMed  Google Scholar 

  13. Gandhi NR, Moll A, Sturm AW et al (2006) Extensively drug-resistant tuberculosis as a cause of death in patients co-infected with tuberculosis and HIV in a rural area of South Africa. Lancet 368:1575–1580. doi:10.1016/S0140-6736(06)69573-1

    Article  PubMed  Google Scholar 

  14. Fang X, Wallqvist A, Reifman J (2009) A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis. BMC Syst Biol 3:92. doi:10.1186/1752-0509-3-92

    Article  PubMed Central  PubMed  Google Scholar 

  15. Segovia-Juarez JL, Ganguli S, Kirschner D (2004) Identifying control mechanisms of granuloma formation during M. tuberculosis infection using an agent-based model. J Theor Biol 231:357–376. doi:10.1016/j.jtbi.2004.06.031

    Article  CAS  PubMed  Google Scholar 

  16. Wigginton JE, Kirschner D (2001) A model to predict cell-mediated immune regulatory mechanisms during human infection with Mycobacterium tuberculosis. J Immunol 166:1951–1967. doi:10.4049/jimmunol.166.3.1951

    Article  CAS  PubMed  Google Scholar 

  17. Gumbo T (2010) New susceptibility breakpoints for first-line antituberculosis drugs based on antimicrobial pharmacokinetic/pharmacodynamic science and population pharmacokinetic variability. Antimicrob Agents Chemother 54:1484–1491. doi:10.1128/AAC.01474-09

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  18. Jayaram R, Gaonkar S, Kaur P et al (2003) Pharmacokinetics-pharmacodynamics of rifampin in an aerosol infection model of tuberculosis. Antimicrob Agents Chemother 47:2118–2124. doi:10.1128/AAC.47.7.2118-2124.2003

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  19. Jayaram R, Shandil RK, Gaonkar S et al (2004) Isoniazid pharmacokinetics-pharmacodynamics in an aerosol infection model of tuberculosis. Antimicrob Agents Chemother 48:2951–2957. doi:10.1128/AAC.48.8.2951-2957.2004

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Rustomjee R, Lienhardt C, Kanyok T et al (2008) A Phase II study of the sterilising activities of ofloxacin, gatifloxacin and moxifloxacin in pulmonary tuberculosis. Int J Tuberc Lung Dis 12:128–138

    CAS  PubMed  Google Scholar 

  21. Davies GR, Brindle R, Khoo SH, Aarons LJ (2006) Use of nonlinear mixed-effects analysis for improved precision of early pharmacodynamic measures in tuberculosis treatment. Antimicrob Agents Chemother 50:3154–3156. doi:10.1128/AAC.00774-05

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  22. O’Sullivan DM, McHugh TD, Gillespie SH (2010) Mapping the fitness of Mycobacterium tuberculosis strains: a complex picture. J Med Microbiol 59:1533–1535. doi:10.1099/jmm.0.019091-0

    Article  PubMed  Google Scholar 

  23. Shorten RJ, McGregor AC, Platt S et al (2013) When is an outbreak not an outbreak? Fit, divergent strains of Mycobacterium tuberculosis display independent evolution of drug resistance in a large London outbreak. J Antimicrob Chemother 68:543–549. doi:10.1093/jac/dks430

    Article  CAS  PubMed  Google Scholar 

  24. Aber VR, Nunn AJ (1978) Short term chemotherapy of tuberculosis. Factors affecting relapse following short term chemotherapy. Bull Int Union Tuberc 53(4):276–280

    Google Scholar 

  25. Perrin FM, Woodward N, Phillips PP, McHugh TD, Nunn AJ, Lipman MC, Gillespie SH (2010) Radiological cavitation, sputum mycobacterial load and treatment response in pulmonary tuberculosis. Int J Tuberc Lung Dis 14(12):1596–1602

    Google Scholar 

  26. Schulzer M, Radhamani MP, Grzybowski S et al (1994) A mathematical model for the prediction of the impact of HIV infection on tuberculosis. Int J Epidemiol 23:400–407

    Article  CAS  PubMed  Google Scholar 

  27. Williams BG, Dye C (2003) Antiretroviral drugs for tuberculosis control in the era of HIV/AIDS. Science 301:1535–1537. doi:10.1126/science.1086845

    Article  CAS  PubMed  Google Scholar 

  28. Wilton P, Smith RD, Coast J, Millar M, Karcher A (2001) Directly observed treatment for multidrug-resistant tuberculosis: an economic evaluation in the United States of America and South Africa. Int J Tuberc Lung Dis 5(12):1137–1142

    Google Scholar 

  29. Murray M (2002) Determinants of cluster distribution in the molecular epidemiology of tuberculosis. Proc Natl Acad Sci U S A 99:1538–1543. doi:10.1073/pnas.022618299

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  30. Corbett EL, Watt CJ, Walker N et al (2003) The growing burden of tuberculosis: global trends and interactions with the HIV epidemic. Arch Intern Med 163:1009–1021. doi:10.1001/archinte.163.9.1009

    Article  PubMed  Google Scholar 

  31. Baltussen R, Floyd K, Dye C (2005) Cost effectiveness analysis of strategies for tuberculosis control in developing countries. BMJ 331:1364. doi:10.1136/bmj.38645.660093.68

    Article  PubMed Central  PubMed  Google Scholar 

  32. Houben RMGJ, Dowdy DW, Vassall A et al (2014) How can mathematical models advance tuberculosis control in high HIV prevalence settings? Int J Tuberc Lung Dis 18:509–514. doi:10.5588/ijtld.13.0773

    Article  PubMed Central  PubMed  Google Scholar 

  33. Kirschner D (1999) Dynamics of co-infection with M. Tuberculosis and HIV-1. Theor Popul Biol 55:94–109. doi:10.1006/tpbi.1998.1382

    Article  CAS  PubMed  Google Scholar 

  34. Magombedze G, Garira W, Mwenje E (2006) Modelling the human immune response mechanisms to Mycobacterium tuberculosis infection in the lungs. Math Biosci Eng 3:661–682

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Medicine, University of St Andrews, Medical and Biological Sciences Building, North Haugh, Fife, St Andrews, KY16 9TF, UK

    Ruth Bowness

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  1. Ruth Bowness
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Correspondence to Ruth Bowness .

Editor information

Editors and Affiliations

  1. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

    Ulf Schmitz

  2. Dept of Systems Biology & Bioinformatics, University of Rostock, Rostock, Germany

    Olaf Wolkenhauer

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Bowness, R. (2016). Systems Medicine and Infection. In: Schmitz, U., Wolkenhauer, O. (eds) Systems Medicine. Methods in Molecular Biology, vol 1386. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-3283-2_7

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  • DOI: https://doi.org/10.1007/978-1-4939-3283-2_7

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-3282-5

  • Online ISBN: 978-1-4939-3283-2

  • eBook Packages: Springer Protocols

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