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Applications of Systems Biology to Improve Pig Health

  • Martine Schroyen
  • Haibo Liu
  • Christopher K. TuggleEmail author
Chapter
  • 518 Downloads

Abstract

In the pig, there are thus far only a handful of examples of health/disease studies approaching a systems biology level analysis, and this is in sharp contrast to the substantial amount of published porcine data on whole genome, transcriptome and proteome experiments with regard to economically important swine diseases. However, systems biology is very powerful since it attempts to understand how these distinct -ome parts work together to create emergent properties that are less likely to be recognized in the analysis of only one component of the system. By integration of the different -omics datasets, systems biology tries to create a more complete understanding of the observed immune response. Until now, such integrative analyses are still in their infancy in terms of application to pig health.

In this chapter, we will cover systems biology tools for network analyses and multilevel data integration, and give examples of their implementation in pig disease studies. Next, we will discuss the need for visualization to interpret the vast amount of data created in -omics studies. Furthermore, the upcoming use of bloodomics is described, since blood is a very relevant immune-related tissue and biomarkers in the blood can easily be assessed and implemented in selection strategies. We conclude with specific examples of -omics and initial systems biology methods on viral (PRRSv) and bacterial (Salmonella) infections, since both agents are economically important pathogens causing disease in pigs and substantial genomics analyses on the response to these pathogens have been conducted to date. In the future, forthcoming consortia such as the FAANG project will accelerate our ability to apply systems biology tools to improving pig health.

Keywords

Quantitative Trait Locus Swine Leukocyte Antigen Bronchial Lymph Node Weight Gain Group PRRSv Infection 
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.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Martine Schroyen
    • 1
  • Haibo Liu
    • 1
  • Christopher K. Tuggle
    • 1
    Email author
  1. 1.Department of Animal ScienceIowa State UniversityAmesUSA

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