Systems Biology, Bioinformatics and Livestock Science

Applications and Future Perspectives of Computational Approaches in Livestock Animals

Author(s): Upasana Pathak, Abhichandan Das, Pranjal Kumar Bora and Sanchaita Rajkhowa * .

Pp: 279-309 (31)

DOI: 10.2174/9789815165616123010018

* (Excluding Mailing and Handling)

Abstract

Livestock is regarded as a critical point of access for enhanced food and nutrition. With the population explosion, an increase in the successful fulfillment of livestock production, including meat and dairy products, is necessary in the most ethical way. Fundamentally keeping the overall nutrition intact along with the health of both human and livestock animals is vital. Although there is an increment in production, it contributes to rising greenhouse gas (methane) emissions, thus damaging the environment. Inheriting novel technologies will not only help in the surplus upliftment of livestock products but also the emission of greenhouse gases. Omics and Systems Biology are such approaches. Omics is a combination of different aspects dealing with complete molecular levels ranging from DNA to protein, protein to metabolites, whereas Systems Biology is the analysis of both mathematical and computational along with biological system modeling. Omics gives a broad overview of both pathways and traits controlling various characters. Thus, showing detailed links between genotype-phenotype. It can yield an enormous amount of data with incredible speed. In addition, Systems Biology lines up to give an overview of the complete biological system rather than just examining a single biological molecule. It combines mathematical modelling, statistics, and bioinformatics for a better grip and understanding of the enormous data sets. In this chapter, we discuss the latest cutting-edge technologies in the field of livestock and how omics can be implemented in creating disease resistant livestock animals without hampering the quality of the products. The chapter also discusses the various applications and future scopes involving computational approaches towards animal science. 


Keywords: Dairy, Deep learning, Livestock, Meat, Metabolomics, Omics, Systems biology, Vaccines.

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