Systems biology integrates the data of all the omics studies and provides the
avenues to understand the biology of an organism at higher levels like at tissue, organ
or organism level. In the last decade, studies of genomics, transcriptomics, proteomics
and metabolomics have been carried out. Only a limited amount of this big data has
been analyzed, which is mainly focused on the genotype (single nucleotide
polymorphism) level like minor allele frequency, copy number variation and structural
variants. The analysis in transcriptomics is limited to differentially expressed genes and
their ontology. Proteomics is focused on virulent factors, proteins involved in the
disease progression and immunomodulation. However, in the case of livestock animals,
there is a need to develop pipelines for the analysis of the omics data. With the
integration of omics data into systems biology studies, there is a need to develop
algorithms to carry out gene interaction and protein interaction studies and to build
interaction networks. The pathway analysis of a system requires the well-defined
interacting hub and edges of the protein system of an organism. Developing AI-ML
models for drug discovery is required to target the pathogens of livestock animals. In
the present era, the research is moving towards single-cell sequencing of the cells and
tissues to explore the genetic heterogeneity in the micro-environment of the tissue and
spatial biology of the tissue. This chapter will introduce the reader to different aspects
of omics technology and its role in systems biology for better livestock management.
Keywords: Database, Genomics, Omics, Proteomics, System biology, Transcriptomics.
