Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-22T17:05:51.026Z Has data issue: false hasContentIssue false
  • English
  • Français

The role of mathematical models of host–pathogen interactions for livestock health and production – a review*

Published online by Cambridge University Press:  26 January 2011

A. B. Doeschl-Wilson
A. B. Doeschl-Wilson*
Affiliation:
Genetics and Genomics, The Roslin Institute and R(D)SVS, University of Edinburgh, Roslin, Midlothian EH25 9PS, UK
*
†E-mail: [email protected]
Get access

Abstract

Compared with the application of mathematical models to study human diseases, models that describe animal responses to pathogen challenges are relatively rare. The aim of this review is to explain and show the role of mathematical host–pathogen interaction models in providing underpinning knowledge for improving animal health and sustaining livestock production. Existing host–pathogen interaction models can be assigned to one of three categories: (i) models of the infection and immune system dynamics, (ii) models that describe the impact of pathogen challenge on health, survival and production and (iii) models that consider the co-evolution of host and pathogen. State-of-the-art approaches are presented and discussed for models belonging to the first two categories only, as they concentrate on the host–pathogen dynamics within individuals. Models of the third category fall more into the class of epidemiological models, which deserve a review by themselves. An extensive review of published models reveals a rich spectrum of methodologies and approaches adopted in different modelling studies, and a strong discrepancy between models concerning diseases in animals and models aimed at tackling diseases in humans (most of which belong to the first category), with the latter being generally more sophisticated. The importance of accounting for the impact of infection not only on health but also on production poses a considerable challenge to the study of host–pathogen interactions in livestock. This has led to relatively simplistic representations of host–pathogen interaction in existing models for livestock diseases. Although these have proven appropriate for investigating hypotheses concerning the relationships between health and production traits, they do not provide predictions of an animal's response to pathogen challenge of sufficient accuracy that would be required for the design of appropriate disease control strategies. A synthesis between the modelling methodologies adopted in categories 1 and 2 would therefore be desirable. The progress achieved in mathematical modelling to study immunological processes relevant to human diseases, together with the current advances in the generation and analysis of biological data related to animal diseases, offers a great opportunity to develop a new generation of host–pathogen interaction models that take on a fundamental role in the study and control of disease in livestock.

Keywords

host–pathogen interactionmathematical modelanimalreviewimmune system dynamics
Type
Review
Information
animal , Volume 5 , Issue 6 , June 2011 , pp. 895 - 910
Copyright
Copyright © The Animal Consortium 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

This review is based on an invited presentation at the 60th Annual Meeting of the European Association for Animal Production held in Barcelona, Spain, in August 2009.

References

Allende, R, Laegreid, WW, Kutish, GF, Galeota, JA, Wills, RW, Osorio, FA 2000. Porcine reproductive and respiratory syndrome virus: description of persistence in individual pigs upon experimental infection. Journal of Virology 74, 10834–10837.CrossRefGoogle ScholarPubMed
Anderson, RM, May, RM 1991. Infectious disease of humans – dynamics and control. Oxford University Press, New York.CrossRefGoogle Scholar
Antia, R, Koella, JC 1994. A model for non-specific immunity. Journal of Theoretical Biology 168, 141–150.CrossRefGoogle Scholar
Antia, R, Koella, JC, Perrot, V 1996. Models of the within-host dynamics of persistent mycobacterial infections. Proceedings of Biological Sciences 263, 257–263.Google ScholarPubMed
Antia, R, Ganusov, VV, Ahmed, R 2005. The role of models in understanding CD8+ T-cell memory. Nature Reviews Immunology 5, 101–111.CrossRefGoogle ScholarPubMed
Ayres, JS, Schneider, DS 2010. The role of anorexia in resistance and tolerance to infections in Drosophila. PLoS ONE Biology 7, e1000150.Google Scholar
Bankroft, AJ, Else, KJ, Grencis, RK 1994. Low-level infection with Trichris muris significantly affects the polarization of the CD4 response. European Journal of Immunology 24, 3113–3118.CrossRefGoogle Scholar
Bauer, AL, Beauchemin, CAA, Perelson, AS 2009. Agent-based modeling of host-pathogen systems: the successes and challenges. Information Sciences 179, 1379–1389.CrossRefGoogle ScholarPubMed
Behnke, JM, Barnard, CJ, Wakelin, D 1992. Understanding chronic nematode infections: evolutionary considerations, current hypotheses and the way forward. International Journal of Parasitology 22, 861–907.Google Scholar
Bishop, SC, Stear, MJ 1997. Modelling responses to selection for resistance to gastro-intestinal parasites in sheep. Animal Science 64, 469–478.CrossRefGoogle Scholar
Celada, F, Seiden, PE 1992. A computer model of cellular interactions in the immune system. Immunology Today 13, 56–61.CrossRefGoogle ScholarPubMed
Coop, RL, Kyriazakis, I 1999. Nutrition–parasite interactions. Veterinary Parasitology 84, 187–204.CrossRefGoogle Scholar
Davenport, MP, Ribeiro, RM, Zhang, L, Wilson, DP, Perelson, AS 2007. Understanding the mechanisms and limitations of immune control of HIV. Immunological Reviews 216, 153–163.CrossRefGoogle ScholarPubMed
Department for Environment Food and Rural Affairs (Defra) 2010. ‘Food 2030’. Retrieved June 28, 2010, from http://www.defra.gov.uk/foodfarm/food/strategy/.Google Scholar
Detilleux, J 2004. Neurophils in the war against Staphylococcus aureus: predator–prey models to the rescue. Journal of Dairy Science 87, 3716–3724.Google Scholar
Detilleux, J, Vangroenweghe, F, Burvenich, C 2006. Mathematical model of the acute inflammatory response to Escherichia coli in intramammary challenge. Journal of Dairy Science 89, 3455–3465.CrossRefGoogle ScholarPubMed
Doeschl-Wilson, AB, Galina-Pantoja, L 2010. Using mathematical models to unravel some mysteries of host–pathogen interaction in mammals: insights from a viral disease in pigs. In Host–pathogen interactions: genetics, immunology and physiology (ed. A Barton), pp. 109–131. Nova Science Publishers, USA.Google Scholar
Doeschl-Wilson, AB, Vagenas, D, Kyriazakis, I, Bishop, SC 2008. Challenging the assumptions underlying genetic variation in host nematode resistance. Genetics Selection and Evolution 40, 241–264.Google Scholar
Doeschl-Wilson, AB, Brindle, W, Emmans, G, Kyriazakis, I 2009a. Unravelling the relationship between animal growth and immune response during micro-parasitic infections. PLoS One 4, e7508.CrossRefGoogle ScholarPubMed
Doeschl-Wilson, AB, Kyriazakis, I, Vincent, A, Rothschild, MF, Thacker, E, Galina-Pantoja, L 2009b. Clinical and pathological responses of pigs from two genetically diverse commercial lines to porcine reproductive and respiratory syndrome virus infection. Journal of Animal Science 87, 1638–1647.CrossRefGoogle ScholarPubMed
Duan, X, Nauwynck, HJ, Pensaert, MB 1997. Effects of origin and state of differentiation and activation of monocytes/macrophages on their susceptibility to porcine reproductive and respiratory syndrome virus (PRRSV). Archives in Virology 142, 2483–2497.Google Scholar
Exton, MS 1997. Infection-induced anorexia: active host defense strategy. Appetite 29, 369–383.CrossRefGoogle Scholar
Forst, CV 2010. Host–pathogen systems biology. In Infectious disease informatics (ed. V Sintchenko), pp. 123–147. Springer, New York.CrossRefGoogle Scholar
Friggens, NC, Newbold, JR 2007. Towards a biological basis for predicting nutrient partitioning: the dairy cow as an example. Animal 1, 87–97.CrossRefGoogle ScholarPubMed
Ganguly, N, Sikdar, BK, Deutsch, A, Canright, G, Chaudhuri, PP 2003. A survey on cellular automata. Technical Report, Centre for High Performance Computing, Dresden University of Technology.Google Scholar
Gaudrealt, N, Rowland, RRR, Wyatt, CR 2009. Factors affecting the permissivness of porcine alveolar macrophages for porcine reproductive and respiratorysyndrome virus. Archives in Virology 154, 133–136.CrossRefGoogle Scholar
Green, DM, Whittemore, CT 2003. Architecture of a harmonized model of the growing pig for the determination of dietary net energy and protein requirements and of excretions into the environment (IMS Pig). Animal Science 77, 113–130.CrossRefGoogle Scholar
Greer, AW 2008. Trade-offs and benefits: implications of promoting a strong immunity to gastro-intestinal parasites in sheep. Parasite Immunology 30, 123–132.Google Scholar
Houdijk, JGM, Bünger, L 2006. Selection for growth increases the penalty of parasitism on growth performance in mice. Proceedings of the Nutritional Society 65, 68A.Google Scholar
Houdijk, JGM, Jessop, NS, Kyriazakis, I 2001. Nutrient partitioning between reproductive and immune functions in animals. Proceedings of the Nutritional Society 60, 515–525.Google Scholar
Houston, AI, McNamara, JM, Barta, Z, Klasing, KC 2007. The effect of energy reserves and food availability on optimal immune defence. Proceedings of the Royal Society B 274, 2835–2842.CrossRefGoogle ScholarPubMed
Jackson, F, Coop, RL 2000. The development of anthelmintic resistance in sheep nematodes. Parasitology 120, 95–107.CrossRefGoogle ScholarPubMed
Janeway, CA, Travers, P, Walport, M 1999. Immunobiology: the immune system in health and disease. Garland Publishing Co., New York.Google Scholar
Katsnelson, A 2010. Well-trained immune cells keep HIV in check. Nature News. doi:10.1038/news.2010.219Google Scholar
Kirschner, DE, Linderman, JJ 2009. Mathematical and computational approaches can complement experimental studies of host pathogen interactions. Cellular Microbiology. 11, 531–539.Google Scholar
Kitano, H 2002. Systems biology: a brief overview. Science 295, 1662–1664.CrossRefGoogle ScholarPubMed
Klasing, KC 2007. Nutrition and the immune system. British Poultry Science 48, 525–537.CrossRefGoogle ScholarPubMed
Kleinstein, SH, Seiden, PE 2000. Simulating the immune system. Computing in Science and Engineering. 2, 69–77.Google Scholar
Knap, PW 1999. Simulation of growth in pigs: evaluation of a model to relate thermoregulation to body protein and lipid content and deposition. Animal Science 68, 655–679.CrossRefGoogle Scholar
Knap, PW, Bishop, SC 2000. Relationship between genetic change and infectious disease in domestic livestock. Occasional publications of the British Society of Animal Science No. 27, pp. 65–80, BSAS, Edinburgh, Scotland.Google Scholar
Kosmrlj, A, Read, EL, Qi, Y, Allen, TM, Altfeld, M, Deeks, SG, Pereyra, F, Carrington, M, Walker, BD, Chakraborty, AK 2010. Effects of thymic selection of the T-cell repertoire on HLA class I-associated control of HIV infection. Nature 465, 350–354.Google Scholar
Kyriazakis, I, Emmans, GC 1999. Voluntary feed intake and diet selection. In Quantitative biology of the pig (ed. I Kyriazakis), pp. 229–248. CABI, Wallingford, Oxon, UK.Google Scholar
Kyriazakis, I, Houdijk, JGM 2007. Food intake and performance of pigs during health, disease and recovery. In Paradigms in pig science (ed. J Wiseman, J Varley, MA McOrist and B Kemp), pp. 493–513. Nottingham University Press, Nottingham, UK.Google Scholar
Kyriazakis, I, Doeschl-Wilson, AB 2009. Anorexia during infection in mammals: variation and its sources. In Voluntary feed intake in pigs (ed. D Torrallardona and E Roura), pp. 307–318. Wageningen Academic Publishers, Netherlands.CrossRefGoogle Scholar
Labarque, G, Van Gucht, S, Nauwynck, H, Van Reeth, K, Pensaert, M 2003. Apoptosis in the lungs of pigs infected with porcine reproductive and respiratory syndrome virus and associations with the production of apoptogenic cytokines. Veterinary Research 34, 249–260.CrossRefGoogle ScholarPubMed
Lescourret, F, Coulon, JB 1994. Modeling the impact of mastitis on milk production in dairy cows. Journal of Dairy Science 77, 2289–2301.Google Scholar
Lochmiller, RL, Deerenberg, C 2000. Trade-offs in evolutionary immunology: just what is the cost of immunity? Oikos 88, 87–98.Google Scholar
Lopez, OJ, Osorio, FA 2004. Role of neutralizing antibodies in PRRSV protective immunity. Veterinary Immunology and Immunopathology 102, 155–163.Google Scholar
Louie, K, Vlassoff, A, Macckay, A 2005. Nematode parasites of sheep: extension of a simple model to include host variability. Parasitology 130, 437–446.Google Scholar
Louzoun, Y 2007. The evolution of mathematical immunology. Immunological Reviews 216, 9–20.Google Scholar
Lundegaard, C, Lund, O, Kesmir, C, Brunak, S, Nielsen, M 2007. Modeling the adaptive immune system: predictions and simulations. Bioinformatics 23, 3265–3275.CrossRefGoogle ScholarPubMed
Mata, J, Cohn, J 2007. Quantitative modeling of immune responses. Immunological Reviews 216, 5–8.Google Scholar
Marchuck, GI, Petrov, RV, Romanyukha, AA, Bocharov, GA 1991. Mathematical model of antiviral immune response. I. Data, analysis, generalized picture construction and parameter evaluation for Hepatitis B. Journal of Theoretical Biology 151, 1–40.CrossRefGoogle Scholar
McEwan, JC, Mason, P, Baker, RL, Clarke, JN, Hickey, SM, Turner, K 1992. Effect of selection for productivity traits on internal parasite resistance in sheep. Proceedings of the New Zealand Society of Animal Production 52, 53–56.Google Scholar
McEwan, JC, Dodds, KG, Gree, GJ, Bain, WE, Duncan, SJ, Wheeler, R, Knowler, KJ, Reid, PJ, Green, RS, Douch, PGC 1995. Genetic estimates for parasite resistance traits in sheep and their correlations with production traits. New Zealand Journal of Zoology 22, 177.Google Scholar
McNamara, JM, Buchanan, KL 2005. Stress, resource allocation, and mortality. Behavioural Ecology 16, 1008–1017.CrossRefGoogle Scholar
Medley, GF 2002. The epidemiological consequences of optimisation of the individual host immune response. Parasitology 125, S61–S70.Google Scholar
Molitor, TW, Bautista, EM, Choi, CS 1997. Immunity to PRRSV: double-edged sword. Veterinary Microbiology 55, 265–276.Google Scholar
Morel, PA 1998. Mathematical modelling of immunological reactions. Frontiers in Bioscience 3, 338–347.CrossRefGoogle ScholarPubMed
Morpurgo, D, Serentha, R, Seiden, P, Celada, F 1995. Modelling thymic functions in a cellular automaton. International Journal of Immunology 7, 505–516.Google Scholar
Mulupuri, P, Zimmerman, JJ, Hermann, J, Johnson, CR, Cano, JP, Yu, W, Dee, SA, Murtaugh, MP 2008. Antigen-specific B-cell responses to porcine peproductive and respiratory syndrome virus infection. Journal of Virology 82, 358–370.CrossRefGoogle Scholar
Murtaugh, MP, Xiao, Z, Zuckermann, FA 2002. Immunological responses of swine to porcine reproductive and respiratory syndrome virus infection. Viral Immunology 15, 533–547.CrossRefGoogle ScholarPubMed
Nowak, MA, Bangham, CR 1996. Population dynamics of immune responses to persistent viruses. Science 272, 74–79.Google Scholar
Nowak, MA, May, RM 1991. Mathematical biology of HIV infections – anitgenic variation and diversity threshold. Mathematical Biosciences 106, 1–21.CrossRefGoogle Scholar
Nowak, MA, May, RM 1993. AIDS pathogenesis – mathematical models of HIV and SIV infections. AIDS 7, S3–S18.CrossRefGoogle ScholarPubMed
Nowak, MA, May, RM 2000. Virus dynamics: mathematical principles of immunology and virology. Oxford University Press, New York.CrossRefGoogle Scholar
Oltenacu, PA, Natzke, RP 1976. Mathematical modeling of the mastitis infection process. Journal of Dairy Science 59, 515–521.CrossRefGoogle ScholarPubMed
Perelson, AS 2002. Modelling viral and immune system dynamics. Nature Reviews Immunology 2, 28–36.Google Scholar
Perelson, AS, Wiegel, FW 1981. Theoretical considerations of the role of antigen structure in B cell activation. Federation Proceedings 40, 1479–1483.Google ScholarPubMed
Perry, B, Grace, D 2009. The impacts of livestock diseases and their control on growth and development processes that are pro-poor. Philosophical Transactions of the Royal Society B 364, 2643–2655.CrossRefGoogle ScholarPubMed
Pugliese, A, Gandolfi, A 2008. A simple model of pathogen-immune dynamics including specific and non-specific immunity. Mathematical Biosciences 214, 73–80.Google Scholar
Rapin, N, Kesmir, C, Frankild, S, Nielsen, M, Lundegaard, C, Brunak, S, Lund, O 2006. Modelling the human immune system by combining bioinformatics and systems biology approaches. Journal of Biological Physics 32, 335–353.Google Scholar
Rauw, WM, Kanis, E, Noordhuizen-Stassen, EN, Grommers, FJ 1998. Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science 56, 15–33.Google Scholar
Renshaw, E 1991. Modelling biological populations in space and time. Cambridge studies in mathematical biology. Cambridge University Press, UK.Google Scholar
Reyes-Umana, V 2010. Assessing key viral determinants for viral load decline. Honours degree thesis, University of Edinburgh, Scotland.Google Scholar
Romanyukha, AA, Rudnev, SG, Sidorov, IA 2006. Energy cost of infection burden: an approach to understanding the dynamics of host–pathogen interactions. Journal of Theoretical Biology 241, 1–13.CrossRefGoogle ScholarPubMed
Sandberg, FB, Emmans, GC, Kyriazakis, I 2006. A model for predicting food intake of growing animals during exposure to pathogens. Journal of Animal Science 84, 1552–1566.CrossRefGoogle Scholar
Sangster, NC 1999. Anthelminitc resistance: past, presence and future. International Journal for Parasitology 29, 115–124.Google Scholar
Segel, LA, Bar-Or, RL 1999. On the role of feedback in promoting conflicting goal of the adaptive immune system. Journal of Immunology 163, 1342–1349.Google Scholar
Seiden, PE, Celada, F 1992. A model for simulating cognate recognition and response in the immune system. Journal of Theoretical Biology 158, 329–340.Google Scholar
Sheldon, BC, Verhulst, S 1996. Ecological immunology: costly parasite defences and trade-offs in evolutionary ecology. Trends in Ecology and Evolution 11, 317–321.CrossRefGoogle ScholarPubMed
Shi, V, Tridane, A, Kuang, Y 2008. A viral load-based cellular automoate approach to modeling HIV dynamics and drug treatment. Journal of Theoretical Biology 253, 24–35.CrossRefGoogle ScholarPubMed
Shudo, E, Iwasa, Y 2001. Inducible defense against pathogens and parasites: optimal choice among multiple options. Journal of Theoretical Biology 209, 233–247.Google Scholar
Simm, G 2010. Guest editorial: livestock and global climate change. Animal 4, 321–322.CrossRefGoogle ScholarPubMed
Smith, AM, Ribeiro, RM 2010. Modeling the viral dynamics of influenza A virus infection. Critical Reviews in Immunology 20, 291–298.CrossRefGoogle Scholar
Souza-e-Silva, H, Savino, W, Feijóo, RA, Vasconcelos, ATR 2009. A cellular automata-based mathematical model for thymocyte development. PLoS ONE 4, e8233.Google Scholar
Vagenas, D, Bishop, SC, Kyriazakis, I 2007a. A model to account for the consequences of host nutrition on the outcome of gastrointestinal parasitism in sheep: logic and concepts. Parasitology 134, 1263–1277.Google Scholar
Vagenas, D, Bishop, SC, Kyriazakis, I 2007b. A model to account for the consequences of host nutrition on the outcome o gastrointestinal parasitism in sheep: model evaluation. Parasitology 134, 1279–1289.CrossRefGoogle Scholar
Vagenas, D, Doeschl-Wilson, AB, Bishop, SC, Kyriazakis, I 2008. In silico exploration of the effects of host genotype and nutrition on the genetic parameters of lambs challenged with gastrointestinal parasites. International Journal of Parasitology 37, 1617–1630.Google Scholar
Van Reeth, K, Nauwynck, H 2000. Proinflammatory cytokines and viral response disease in pigs. Veterinary Research 31, 187–213.Google Scholar
Van der Waaij, EH 2004. A resource allocation model describing the consequences of artificial selection under metabolic stress. Journal of Animal Science 82, 973–981.CrossRefGoogle ScholarPubMed
Van der Waaij, EH, Bijma, P, Bishop, SC, van Arendonk, JAM 2000. Modeling selection for production traits under constant infection pressure. Journal of Animal Science 78, 2809–2820.Google Scholar
Wellock, IJ, Emmans, GC, Kyriazakis, I 2003. Predicting the consequences of social stressors on pig food intake and performance. Journal of Animal Science 81, 2995–3007.CrossRefGoogle ScholarPubMed
White, LJ, Schukken, YH, Dogan, B, Green, LE, Döpfer, D, Chappell, MJ, Medley, G 2010. Modelling the dynamics of intramammary E. coli infections in dairy cows: understanding mechanisms that distinguish transient from persistent infections. Veterinary Research 41, 13.CrossRefGoogle ScholarPubMed
Wodarz, D 2003. Hepatitis C virus dynamics and pathology: the role of CTL and antibody responses. Journal of General Virology 84, 1743–1750.Google Scholar
Wodarz, D, Lloyd, AL, Jansen, AA, Nowak, MA 1999. Dynamics of macrophage and T cell infection of HIV. Journal of Theoretical Biology 196, 101–113.Google Scholar
Wolfram, S 1994. Cellular automata and complexity. Addison Wesley, New York, USA.Google Scholar
Wood, JC, McKendrick, IJ, Gettingby, G 2006a. A simulation model for the study of the within-animal infection dynamics of E. coli O157. Preventative Veterinary Medicine 74, 180–193.CrossRefGoogle Scholar
Wood, JC, McKendrick, IJ, Gettingby, G 2006b. Assessing the efficacy of within-animal control strategies against E. coli O157: a simulation study. Preventative Veterinary Medicine 74, 194–211.CrossRefGoogle ScholarPubMed
Woolhouse, MEJ, Webster, JP, Domingo, E, Charlesworth, B, Levin, BR 2002. Biological and biomedical implications of the co-evolution of pathogens and their hosts. Nature 32, 569–577.Google ScholarPubMed
Xiao, Z, Batista, L, Dee, S, Halbur, P, Murtaugh, MP 2004. The level of virus-specific T-cell and macrophage recruitment in porcine reproductive and respiratory syndrome virus infection in pigs is independent of virus load. Journal of Virology 78, 5923–5933.CrossRefGoogle ScholarPubMed
Yates, A, Chan, CC, Callard, RE, George, AJ, Stark, J 2001. An approach to modelling in immunology. Briefings in Bioinformatics 2, 245–257.CrossRefGoogle ScholarPubMed
Zaralis, K, Tolkamp, BJ, Houdijk, JGK, Wylie, ARG, Kyriazakis, I 2008. Changes in food intake and circulating leptin due to gastrointestinal parasitism in lambs of two breeds. Journal of Animal Science 86, 1891–1903.Google Scholar
Zimmerman, J, Benfield, DA, Murtaugh, MP, Osorio, F, Stevenson, GW, Torremorell, M 2006. Porcine reproductive and respiratory virus (porcine arterivirus). In Diseases of swine, 9th edition, (ed. BE Straw, JE Zimmerman, S l'Allaire and DJ Taylor), Wiley-Blackwell, UK.Google Scholar
Zorzenon dos Santos, RM, Coutinho, S 2001. Dynamics of HIV infection: a cellular automata approach. Physical Review Letters 87, 102–168.Google Scholar