Skip to main content
×
Home
    • Aa
    • Aa

Systems biology in animal sciences

  • H. Woelders (a1), M. F. W. Te Pas (a1), A. Bannink (a2), R. F. Veerkamp (a1) and M. A. Smits (a1)...
Abstract

Systems biology is a rapidly expanding field of research and is applied in a number of biological disciplines. In animal sciences, omics approaches are increasingly used, yielding vast amounts of data, but systems biology approaches to extract understanding from these data of biological processes and animal traits are not yet frequently used. This paper aims to explain what systems biology is and which areas of animal sciences could benefit from systems biology approaches. Systems biology aims to understand whole biological systems working as a unit, rather than investigating their individual components. Therefore, systems biology can be considered a holistic approach, as opposed to reductionism. The recently developed ‘omics’ technologies enable biological sciences to characterize the molecular components of life with ever increasing speed, yielding vast amounts of data. However, biological functions do not follow from the simple addition of the properties of system components, but rather arise from the dynamic interactions of these components. Systems biology combines statistics, bioinformatics and mathematical modeling to integrate and analyze large amounts of data in order to extract a better understanding of the biology from these huge data sets and to predict the behavior of biological systems. A ‘system’ approach and mathematical modeling in biological sciences are not new in itself, as they were used in biochemistry, physiology and genetics long before the name systems biology was coined. However, the present combination of mass biological data and of computational and modeling tools is unprecedented and truly represents a major paradigm shift in biology. Significant advances have been made using systems biology approaches, especially in the field of bacterial and eukaryotic cells and in human medicine. Similarly, progress is being made with ‘system approaches’ in animal sciences, providing exciting opportunities to predict and modulate animal traits.

    • Send article to Kindle

      To send this article to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.

      Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

      Find out more about the Kindle Personal Document Service.

      Systems biology in animal sciences
      Available formats
      ×
      Send article to Dropbox

      To send this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about sending content to Dropbox.

      Systems biology in animal sciences
      Available formats
      ×
      Send article to Google Drive

      To send this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about sending content to Google Drive.

      Systems biology in animal sciences
      Available formats
      ×
Copyright
Corresponding author
E-mail: henri.woelders@wur.nl
References
Hide All
Aksenov SV, Church B, Dhiman A, Georgieva A, Sarangapani R, Helmlinger G, Khalil IG 2005. An integrated approach for inference and mechanistic modeling for advancing drug development. FEBS Letters 579, 18781883.
Amit I, Garber M, Chevrier N, Leite AP, Donner Y, Eisenhaure T, Guttman M, Grenier JK, Li W, Zuk O, Schubert LA, Birditt B, Shay T, Goren A, Zhang X, Smith Z, Deering R, McDonald RC, Cabili M, Bernstein BE, Rinn JL, Meissner A, Root DE, Hacohen N, Regev A 2009. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257263.
Arakelyan L, Vainstein V, Agur Z 2002. A computer algorithm describing the process of vessel formation and maturation, and its use for predicting the effects of anti-angiogenic and anti-maturation therapy on vascular tumor growth. Angiogenesis 5, 203214.
Asslaber M, Zatloukal K 2007. Biobanks: transnational, European and global networks. Briefings in Functional Genomics & Proteomics 6, 193201.
Bannink A, Kogut J, Dijkstra J, France J, Kebreab E, Van Vuuren AM, Tamminga S 2006. Estimation of the stoichiometry of volatile fatty acid production in the rumen of lactating cows. Journal of Theoretical Biology 238, 3651.
Bionaz M, Loor JJ 2008. Gene networks driving bovine milk fat synthesis during the lactation cycle. BMC Genomics 9, 366.
Boer HMT, Stötzel C, Röblitz S, Deuflhard P, Veerkamp RF, Woelders H 2010. Mathematical model of the bovine oestrous cycle. In Food, feed, energy and fibre from land, a vision for 2020. Annual Conference of the British Society of Animal Science, Agricultural Research Forum and the World Poultry Science Association, Belfast, UK, p. 163.
Bruggeman FJ, Westerhoff HV 2007. The nature of systems biology. Trends in Microbiology 15, 4550.
Calus MP, Meuwissen TH, de Roos AP, Veerkamp RF 2008. Accuracy of genomic selection using different methods to define haplotypes. Genetics 178, 553561.
Feist AM, Zielinski DC, Orth JD, Schellenberger J, Herrgard MJ, Palsson BØ 2010. Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli. Metabolic Engineering 12, 173186.
Fell DA, Small JR 1986. Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochemical Journal 238, 781786.
Finney A, Hucka M, Bornstein BJ, Keating SM, Shapiro BE, Matthews J, Kovitz BL, Schilstra MJ, Funahashi A, Doyle JC, Kitano H 2006. Software infrastructure for effective communication and reuse of computational models. In System modeling in cellular biology: from concepts to nuts and bolts (ed. Z Szallasi, J Stelling and P Periwal), pp. 355378. MIT Press, Cambridge, MA, USA.
Fox S, Filichkin S, Mockler TC 2009. Applications of ultra-high-throughput sequencing. Methods in Molecular Biology 553, 79108.
Gibson JP, Bishop SC 2005. Use of molecular markers to enhance resistance of livestock to disease: a global approach. Revue scientifique et technique (International Office of Epizootics) 24, 343353.
Haanstra JR, van Tuijl A, Kessler P, Reijnders W, Michels PA, Westerhoff HV, Parsons M, Bakker BM 2008. Compartmentation prevents a lethal turbo-explosion of glycolysis in trypanosomes. Proceedings of the National Academy of Sciences of the United States of America 105, 1771817723.
Hunter P, Nielsen P 2005. A strategy for integrative computational physiology. Physiology (Bethesda) 20, 316325.
Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L 2001. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 292, 929934.
Joyce AR, Palsson BO 2006. The model organism as a system: integrating ‘omics’ data sets. Nature Reviews Molecular Cell Biology 7, 198210.
Khalil IG, Hill C 2005. Systems biology for cancer. Current Opinion in Oncology 17, 4448.
Kholodenko BN 2006. Cell-signalling dynamics in time and space. Nature Reviews Molecular Cell Biology 7, 165176.
Kitano H 2000. Perspectives on systems biology. New Generation Computing 18, 199216.
Kittler R, Pelletier L, Buchholz F 2008. Systems biology of mammalian cell division. Cell Cycle 7, 21232128.
Klauschen F, Angermann BR, Meier-Schellersheim M 2007. Understanding diseases by mouse click: the promise and potential of computational approaches in Systems Biology. Clinical and Experimental Immunology 149, 424429.
Kommadath A, Mulder HA, de Wit AAC, Woelders H, Smits MA, Beerda B, Veerkamp RF, Frijters ACJ, te Pas MFW 2010. Gene expression patterns in anterior pituitary associated with quantitative measure of oestrous behaviour in dairy cows. Animal 4, 12971307.
Lehner B 2007. Modelling genotype-phenotype relationships and human disease with genetic interaction networks. Journal of Experimental Biology 210, 15591566.
Lippolis JD, Reinhardt TA 2008. Centennial paper: proteomics in animal science. Journal of Animal Science 86, 24302441.
Megason SG, Fraser SE 2007. Imaging in systems biology. Cell 130, 784795.
Meuwissen TH, Hayes BJ, Goddard ME 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 18191829.
Noble D 2008. Computational models of the heart and their use in assessing the actions of drugs. Journal of Pharmacological Sciences 107, 107117.
Pallen MJ, Wren BW 2007. Bacterial pathogenomics. Nature 449, 835842.
Palsson B 2002. In silico biology through “omics”. Nature Biotechnology 20, 649650.
Pryce JE, Royal MD, Garnsworthy PC, Mao IL 2004. Fertility in the high-producing dairy cow. Livestock Production Science 86, 125135.
Quackenbush J 2007. Extracting biology from high-dimensional biological data. Journal of Experimental Biology 210, 15071517.
Raghunathan A, Reed J, Shin S, Palsson B, Daefler S 2009. Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host-pathogen interaction. BMC Systems Biology 3, 38.
Reinecke I, Deuflhard P 2007. A complex mathematical model of the human menstrual cycle. Journal of Theoretical Biology 247, 303330.
Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, Guhathakurta D, Sieberts SK, Monks S, Reitman M, Zhang C, Lum PY, Leonardson A, Thieringer R, Metzger JM, Yang L, Castle J, Zhu H, Kash SF, Drake TA, Sachs A, Lusis AJ 2005. An integrative genomics approach to infer causal associations between gene expression and disease. Nature Genetics 37, 710717.
Schokker D, Smits MA, Hoekman AJ, Parmentier HK, Rebel JM 2010. Effects of Salmonella on spatial-temporal processes of jejunal development in chickens. Developmental & Comparative Immunology 34, 10901100.
Schuetz R, Kuepfer L, Sauer U 2007. Systematic evaluation of objective functions for predicting intracellular fluxes in Escherichia coli. Molecular Systems Biology 3, 119.
Shav-Tal Y, Singer RH, Darzacq X 2004. Imaging gene expression in single living cells. Nature Reviews Molecular Cell Biology 5, 855861.
Shorten PR, Pleasants TB, Upreti GC 2004. A mathematical model for mammary fatty acid synthesis and triglyceride assembly: the role of stearoyl CoA desaturase (SCD). Journal of Dairy Research 71, 385397.
Simao E, Remy E, Thieffry D, Chaouiya C 2005. Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in E. coli. Bioinformatics 21 (Suppl. 2), ii190ii196.
Snoep JL 2005. The silicon cell initiative: working towards a detailed kinetic description at the cellular level. Current Opinion in Biotechnology 16, 336343.
Sordillo LM, Contreras GA, Aitken SL 2009. Metabolic factors affecting the inflammatory response of periparturient dairy cows. Animal Health Research Reviews/Conference of Research Workers in Animal Diseases 10, 5363.
Te Pas MFW, van Hemert S, Hulsegge B, Hoekman AJW, Pool MH, Rebel JMJ, Smits MA 2008. A pathway analysis tool for analyzing microarray data of species with low physiological information. Advances in Bioinformatics 2008 Article ID 719468, 17.
van Eunen K, Bouwman J, Daran-Lapujade P, Postmus J, Canelas AB, Mensonides FI, Orij R, Tuzun I, van den Brink J, Smits GJ, van Gulik WM, Brul S, Heijnen JJ, de Winde JH, Teixeira de Mattos MJ, Kettner C, Nielsen J, Westerhoff HV, Bakker BM 2010. Measuring enzyme activities under standardized in vivo-like conditions for systems biology. The FEBS Journal 277, 749760.
van Hemert S, Hoekman AJ, Smits MA, Rebel JM 2006. Gene expression responses to a Salmonella infection in the chicken intestine differ between lines. Veterinary Immunology and Immunopathology 114, 247258.
Veerkamp RF, Beerda B, van der Lende T 2003. Effects of genetic selection for milk yield on energy balance, levels of hormones, and metabolites in lactating cattle, and possible links to reduced fertility. Livestock Production Science 83, 257275.
Wiltbank M, Lopez H, Sartori R, Sangsritavong S, Gumen A 2006. Changes in reproductive physiology of lactating dairy cows due to elevated steroid metabolism. Theriogenology 65, 1729.
Wunder F, Kalthof B, Muller T, Huser J 2008. Functional cell-based assays in microliter volumes for ultra-high throughput screening. Combinatorial Chemistry & High Throughput Screening 11, 495504.
Yashiro Y, Bannai H, Minowa T, Yabiku T, Miyano S, Osawa M, Iwama A, Nakauchi H 2009. Transcriptional profiling of hematopoietic stem cells by high-throughput sequencing. International Journal of Hematology 89, 2433.
Young D, Stark J, Kirschner D 2008. Systems biology of persistent infection: tuberculosis as a case study. Nature reviews. Microbiology 6, 520528.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

animal
  • ISSN: 1751-7311
  • EISSN: 1751-732X
  • URL: /core/journals/animal
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Keywords:

Metrics

Full text views

Total number of HTML views: 7
Total number of PDF views: 85 *
Loading metrics...

Abstract views

Total abstract views: 164 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 23rd October 2017. This data will be updated every 24 hours.