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.

Systems biology intends to portray as well as comprehend biology around the globe, where biological processes are acknowledged as the outcome of complex mechanisms which occur on multiple dimensions beginning with the molecular level and reaching to ecosystem level. Biological information in systems biology comes from overlying but distinct scientific areas, each with its own style of expressing the events under research. Simulation and modeling are computer-aided methods that are precious for the quantitative and integrative description, prediction, and exploration of these mechanisms. In addition, Multi-level and hybrid models have been developed to meet both improved accuracy and capability of making good knowledge bases, which turned out to be a valuable tool in computational systems biology. Various methods, including the silicon model, have been developed in many scientific disciplines for solving multi-scale problems, which is appropriate to continuum-based modeling strategies. The association between system properties is depicted using continuous mathematical equations in which heterogeneous microscopic elements, such as persons, are modelled using individual units. We summarized multi-scale methodologies and their application in biotechnology and drug development applications in view of emphasizing the importance of studying systems as a whole with the role of artificial intelligence and biostatistical aspects in this review. 

Scientific understanding has rapidly expanded in the new biological age, with the rapid advancement of genomic science and molecular biology, It is a challenge to reintegrate the enormous quantity of information and data that was generated from works related to genomics, transcriptomics, proteomics, and metabolomics in order to effectively explain the organism and connect molecular processes with higher-level biological phenomena. Scientific understanding has expanded quickly in the new biological age due to the rapid advancement of genomic science and molecular biology. This inspired contemporary interest in systems biology, which investigates organisms as integrated systems made up of dynamic and interconnected genetic, protein, metabolic, and cellular components using biology, mathematics, biophysics, biochemistry, bioinformatics, and computer science. Systems biology is the key concept underlying Physiome, a mathematical measure of how an organism functions in normal and pathologic states which is based on morphome. The simulation models based on mathematical expressions and physics can aid in the interpretation and encapsulation of biological phenomena in a computable and repeatable manner. Researchers have created tools and standards to allow the reproducibility and reuse of mathematical models of biological systems, as well as tools and guidelines to promote semantic representation of computational models and repositories where models can be archived, shared, and discovered. 

Systems biology is concerned with complex interactions in biological systems, employing a holistic manner in addition to classical reductionism. Systems biology uses statistics, computational biology, and mathematical modelling to integrate and analyse vast data sets to obtain a better knowledge of biology and predict the behaviour of biological systems. It has gained attention in fisheries because of its ability to uncover novel processes. It can generate a panorama of events that occur within fish. In a systems biology approach, data from fish genomics, transcriptomics, proteomics, and metabolomics are integrated, allowing for a comprehensive understanding of dynamic systems with varying degrees of biological organisation. Protein-protein interactions help us understand the systematic mechanisms underlying overall growth, development, physiology, and disease in fish. Systems biology and omics techniques are being applied in a variety of fisheries studies such as species identification, understanding the processes of infection and stress tolerance, fishpathogen interactions, fish disease diagnostics and disease control, the impact of environmental factors on fish, and determining the fish's response to these, identification of gene sequences and biomarkers. Except for a few pioneering applications of system biology to Fisheries, this approach to fisheries research is still in its infancy stage. Systems biology has the potential to provide solutions to the diverse issues of fisheries.

With the recent advances in high throughput next-generation sequencing technologies and bioinformatics approach, gut microbiome research, especially in livestock species, has expanded immensely, elucidating the greatest potential to investigate the unacknowledged understanding of rumen microbiota in host physiology at the molecular level. The association of a complex aggregated community of microbes to host metabolism is of great importance due to their crucial participation in metabolic, immunological, and physiological tasks. The knowledge of this sophisticated network of a symbiotic association of gut microbiota to host organisms may lead to novel insights for improving health, enhancing production, and reducing the risk of disease progression in livestock species necessary to meet the demands of the human race. The full picture of microorganisms present in a particular area can be achieved with the help of culture-independent omics-based approaches. The integration of metagenomics, metatranscriptomics, metaproteomics, and meta-metabolomics technologies with systems biology emphasizes the taxonomic composition, identification, functional characterization, gene abundance, metabolic profiling, and phylogenetic information of microbial population along with the underlying mechanism for pathological processes and their involvement as probiotic. The rumen secretions or partially digested feed particles, as well as fecal samples, are generally employed for gut microbiome investigation. The 16S rRNA gene sequencing amplicon-based technology is the most employed technique for microbiome profiling in livestock species to date. The use of software and biological databases in the field of gut microbiome research gives an accurate in-depth analysis of the microbial population greatly. 

India has an extensive livestock wealth with a growing rate of 6% per annum with a crucial role in the Indian economy. The livestock sector is one of the important subsectors of agriculture, which contributes 25.6% of total agriculture GDP. The arrival of deep sequencing technologies such as Next Generation Sequencing (NGS) and Single Cell Sequencing (SCS) has produced huge sequence data that can be exploited to advance well being, health, reproduction and yield of livestocks by employment of integrated omics strategies. The current era of omics, i.e., genomics, transcriptomics, proteomics, metabolomics, translatomics and single-cell sequencing, has considerably improved researcher's understanding of livestock research at the gene level and opened new avenues in terms of single-cell studies, which need to be carried out in the near future. NGS plays a crucial role in understanding the genetic mechanism of animal’s functions and its interaction with the environment. Furthermore, the SCS will provide insight into the functions of cell types in livestock species. The data generated using NGS and SCS approaches may help to discover novel molecular markers from the complete genome and develop global diagnostic methods for the detection of infectious diseases and their agents.

With the increasing human population, livestock farming has been intensified over the years to support different products from farm animals. Hence, the requirement to monitor livestock diseases becomes critical. In particular, outbreaks due to viral diseases are a major concern for the livestock industry worldwide. It has been observed that close interaction of humans-livestock could lead to transboundary diseases. Hence detection of potential viral pathogens requires a deeper understanding of the livestock virome. The rapid development of bioinformatics and computational tools, as well as advances in Next-Generation Sequencing (NGS) technologies, has opened up new options for infectious disease surveillance in terms of both quality and scale. The phrase “systems biology” has just been recently adopted to define cutting-edge cross-disciplinary biology research. Synthetic biology, integrative biology, systems biomedicine, and metagenomics are some of the growing post-genomic domains that intersect with systems biology. Systems biology represents a paradigm shift in biology and medicine from many perspectives by incorporating a new culture that acknowledges the dynamic and interdependent interactions of the complex network of genes and their associated proteins in order to gain a systematic understanding of biology, health, and disease. By enhancing our understanding of viral disease development, diagnosis, prevention, and therapy, the application of systems biology to human and veterinary medicine has the potential to transform healthcare. The current chapter focuses on examples of various viral diseases associated with livestock animals and the role of systems biology approaches to understand them. 

Proteomics is a branch of science that allows us to study a whole expressed protein pool from a cell or tissue. This has been helpful for many years in studying microbial makeup, but in animals, this field has not been explored much due to factors like the complexity and variation in genes of every cell depending upon their specialized function and tissue organization. However, in recent years many new techniques have been introduced in this area, which has added to the plethora of knowledge about animal proteins and has made it easy to understand the diseases and health-related aspects of livestock science. In this chapter, we will discuss the new advancements in animal proteomics to discover the protein pool from the different animal species of interest, branches of proteomics, and their role in livestock health and diseases.

Nano-materials were utilized as therapeutics and diagnostics agents in the context of human medicine. However, the application of nanoparticles in the field of livestock animals is still at a nascent stage. The proper utilization of nanoparticles in livestock sciences, such as improvement in milk production, diagnosis of varied diseases, delivery of nutrients and/or in their reproduction, offers prospective outcomes which have direct implications to meet the ever-growing human populations. Further, with the advent of high throughput omics technologies, noteworthy development in the past decades has paved the way to advanced systems biology area. The high throughput data handling from diverse omics methodologies and making a holistic interpretation posed a challenge, moreover, to connect the dots and present a larger picture of the intricate network level data, systems biology comes to the rescue. The design and advancement in different algorithms of systems biology tools seldom help one to integrate multi-layered data. Systems biology is applied to livestock animals and poultry for their overall development and/or risk assessment for their diseases. In this chapter, we discussed the implementation of nanobiotechnology and systems biology approaches to livestock animals. We illustrated a few examples of how the application of nanotech and systems biology improved some desired qualities in livestock. This chapter summarizes the ongoing research and efforts of different groups, along with the future prospects of innovative technologies in the area of nanotech and systems biology. 

Single cell RNA sequencing (ScRNAseq) is in its infancy. There are limited studies in which this technique has been implemented to solve the scientific problem. ScRNAseq involves well facilitated labs and high end computing facilities. The ScRNAseq studies were mainly carried out in the clinical and biomedical areas. These studies are carried out in cancer research, which involves the role of immune genes or immunotherapy for cancer treatment. The human cell atlas programme is going on and atlases for different human cells are being released as it is completed. However, in the case of livestock animals, it has just started. In India, there are few ScRNAseq studies that have focused on the different developmental stages of buffalo. The experimental and bioinformatics analysis ScRNAseq involves various steps. Among this, the alignment of reads to reference genome/transcriptome is important. There is a need to develop a standardized reference genome/transcriptome for each type of cell present in different domestic/commercial livestock. Once we have all the valuable information from ScRNAseq, then this data can be integrated with system biology approaches to understand the cellular processes at a larger scale. This integration of interdisciplinary sciences will enhance the production, quality and health of the livestock animals and may help for sustainable management of livestock. 

Advanced research methods have enhanced the productivity and problem solving abilities of scientific development in the field of drug designing and discovery. Various diseases have been problematic for the survival of human civilisation and livestock. Available methods that can provide results for diseases include; computer aided drug designing, system biology, and machine learning. Due to the diversity of livestock and multiple disease types, robust methods are required for drug discovery. Artificial intelligence has paved the way for faster problem solving innovations and discoveries in multiple aspects, such as economics, engineering, and healthcare. Systems biology plays a pivotal role in the biological evaluation of living beings. System-level understanding of livestock animals is the need of the hour for effective drug discovery, which includes genomic, proteomic, enzymatic, and metabolic pathways involved in a biological system. Livestock deaths due to diseases are reported worldwide, which creates a demand for drug discovery solutions. Multiple diseases for various livestock have been investigated, and drug discovery has been a great relief for those specific diseases. In this context, we have communicated about the integration of all the above mentioned aspects (artificial intelligence, machine learning, systems biology, drug discovery) to come up with a better resolution for the livestock in terms of drug development. 

The recurrent and comprehensive study of biological systems as a single entity in response to stimuli is known as systems biology. The introduction of high-throughput technology for studying an animal's DNA, proteome, and metabolome was a blow to reductionism in livestock science. It is based on ideas formalized in models derived from global functional genomics investigations of the genome, transcriptome, proteome, metabolome, and other complex biological systems. The mapping of entire sets of genes, transcripts, proteins, and metabolites from a variety of organisms has driven the creation of novel '-omic' technologies for gathering and analyzing vast amounts of data. This widely defined systems approach is being used to address a wide range of issues and organizational scales, along with several elements of livestock research. It is well established that the tools that relate genetic variations to their cellular activities, pathways, and other biological roles will become even more essential in the future. For each animal genomics research issue, a vision, current state of the art, research needed to progress the field, expected outputs, and partnerships are required. Modern computational tools capable of finding functional implications and biologically meaningful networks complement the ever-increasing ability to generate massive molecular, microbial, and metabolite data sets. The intricate inter-tissue responses to physiological status and nutrition can now be seen at the same time. The knowledge acquired from the application of functional analysis of systems biology data sets to livestock management in order to improve productivity, quality, and yield. 

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.