Overview of Systems Biology and Omics Technologies

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on “omics” driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in “omics” technologies, compare the pros and cons of the different “omics” technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of “omics” technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of “omics” technologies to agricultural and food biotechnology, and review the impact of “omics” on current COVID-19 research.

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Systems biology is a method for analyzing massive amounts of multidimensional data generated by omics technologies. Cross-validation of the various technological platforms is critical

10.31219/osf.io/p8vkd ◽

2021 ◽

Author(s):

Moataz Dowaidar

Keyword(s):

Systems Biology ◽

Experimental Design ◽

Network Theory ◽

Multidimensional Data ◽

Tissue Analysis ◽

William Of Ockham ◽

Omics Technologies ◽

Network Change ◽

Emergent Systems ◽

Technological Platforms

The value of systems biology in cardiology is becoming more recognized. There has been a tremendous rise in the number of articles in the last two decades, as publicly available datasets have been provided online and high-throughput tissue analysis has become more prevalent. In animal models, however, the future of cardiovascular medicine is less likely to be reanalyzing data and more likely to be investigating the function of GWAS-identified SNPs or network change using informatics and gene-editing technologies. These techniques, when combined with other omics interrogations and rigorous experimental design, have the potential to improve our understanding of gene-to-disease pathways.Systems biology is a method for studying large amounts of multidimensional data generated by omics technologies and, more broadly, the transition to big data in health care.Cross-validation of the various technological platforms is critical because omics studies are prone to bias and overinterpretation.Investigators must carefully determine which publicly accessible datasets, if any, to employ while conducting a systems analysis. Despite the fact that network theory and machine learning may yield amazing outcomes, these methods are not yet standardized. The studies mentioned here are excellent examples, in part because they use empirical models to support emergent systems biology results. In the few successful cases, careful experimental design, including interventional research and clinical trials, is required, in addition to the insights supplied by bioinformatics analysis of omics approaches. While it may be tempting to use emergent qualities to capture these new discoveries in more fundamental concepts, we agree with the English philosopher William of Ockham when he says, "It is futile to do with more things what can be done with fewer."

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Omics Technologies Used in Systems Biology

Systems Biology in Toxicology and Environmental Health ◽

10.1016/b978-0-12-801564-3.00004-3 ◽

2015 ◽

pp. 57-83 ◽

Cited By ~ 3

Author(s):

Delisha Stewart ◽

Suraj Dhungana ◽

Robert Clark ◽

Wimal Pathmasiri ◽

Susan McRitchie ◽

...

Keyword(s):

Systems Biology ◽

Omics Technologies

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The potential for mitigation of methane emissions in ruminants through the application of metagenomics, metabolomics, and other -OMICS technologies

Journal of Animal Science ◽

10.1093/jas/skab193 ◽

2021 ◽

Vol 99 (10) ◽

Author(s):

Victoria Asselstine ◽

Stephanie Lam ◽

Filippo Miglior ◽

Luiz F Brito ◽

Hannah Sweett ◽

...

Keyword(s):

Systems Biology ◽

Genomic Selection ◽

Management Practices ◽

Biological Information ◽

Physiological Status ◽

Omics Technologies ◽

Ch4 Emissions ◽

Ch4 Production ◽

Genetic Mechanisms ◽

Selection For

Abstract Ruminant supply chains contribute 5.7 gigatons of CO2-eq per annum, which represents approximately 80% of the livestock sector emissions. One of the largest sources of emission in the ruminant sector is methane (CH4), accounting for approximately 40% of the sectors total emissions. With climate change being a growing concern, emphasis is being put on reducing greenhouse gas emissions, including those from ruminant production. Various genetic and environmental factors influence cattle CH4 production, such as breed, genetic makeup, diet, management practices, and physiological status of the host. The influence of genetic variability on CH4 yield in ruminants indicates that genomic selection for reduced CH4 emissions is possible. Although the microbiology of CH4 production has been studied, further research is needed to identify key differences in the host and microbiome genomes and how they interact with one another. The advancement of “-omics” technologies, such as metabolomics and metagenomics, may provide valuable information in this regard. Improved understanding of genetic mechanisms associated with CH4 production and the interaction between the microbiome profile and host genetics will increase the rate of genetic progress for reduced CH4 emissions. Through a systems biology approach, various “-omics” technologies can be combined to unravel genomic regions and genetic markers associated with CH4 production, which can then be used in selective breeding programs. This comprehensive review discusses current challenges in applying genomic selection for reduced CH4 emissions, and the potential for “-omics” technologies, especially metabolomics and metagenomics, to minimize such challenges. The integration and evaluation of different levels of biological information using a systems biology approach is also discussed, which can assist in understanding the underlying genetic mechanisms and biology of CH4 production traits in ruminants and aid in reducing agriculture’s overall environmental footprint.

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Plant Proteomic Research 4.0: Frontiers in Stress Resilience

International Journal of Molecular Sciences ◽

10.3390/ijms222413362 ◽

2021 ◽

Vol 22 (24) ◽

pp. 13362

Author(s):

Sixue Chen ◽

Setsuko Komatsu

Keyword(s):

Systems Biology ◽

High Throughput ◽

Large Scale ◽

Stress Resilience ◽

Omics Technologies ◽

Proteomic Research

Large-scale high-throughput multi-omics technologies are indispensable components of systems biology in terms of discovering and defining parts of the system [...]

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Applications of Multi-Omics Technologies for Crop Improvement

Frontiers in Plant Science ◽

10.3389/fpls.2021.563953 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yaodong Yang ◽

Mumtaz Ali Saand ◽

Liyun Huang ◽

Walid Badawy Abdelaal ◽

Jun Zhang ◽

...

Keyword(s):

Systems Biology ◽

Complex Traits ◽

Crop Improvement ◽

Biotic And Abiotic Stress ◽

Crop Breeding ◽

Omics Technologies ◽

Crop Genetics ◽

Phenotype Model ◽

Next Generation Sequencing Ngs ◽

New Generation

Multiple “omics” approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the “phenotype to genotype” and “genotype to phenotype” concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.

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Metabolomics and fluxomics approaches

Essays in Biochemistry ◽

10.1042/bse0450067 ◽

2008 ◽

Vol 45 ◽

pp. 67-82 ◽

Cited By ~ 72

Author(s):

Marta Cascante ◽

Silvia Marin

Keyword(s):

Systems Biology ◽

Stable Isotope ◽

Metabolic Networks ◽

Cell Metabolism ◽

Computational Techniques ◽

Omics Technologies ◽

Complete Set ◽

Functional Operation ◽

Comprehensive Characterization

Systems biology is based on the understanding that the behaviour of the whole is greater than would be expected from the sum of its parts. Thus the ultimate goal of systems biology is to predict the behaviour of the whole system on the basis of the list of components involved. Recent advances in ‘-omics’ technologies and the development of new computational techniques and algorithms have greatly contributed to progress in this field of biology. Among the main ‘-omics’ technologies, metabolomics is expected to play a significant role in bridging the phenotype–genotype gap, since it amplifies changes in the proteome and provides a better representation of the phenotype of an organism than other methods. However, knowledge of the complete set of metabolites is not enough to predict the phenotype, especially for higher cells in which the distinct metabolic processes involved in their production and degradation are finely regulated and interconnected. In these cases, quantitative knowledge of intracellular fluxes is required for a comprehensive characterization of metabolic networks and their functional operation. These intracellular fluxes cannot be detected directly, but can be estimated through interpretation of stable isotope patterns in metabolites. Moreover, analysis of these fluxes by means of metabolic control theories offers a potentially unifying, holistic paradigm to explain the regulation of cell metabolism. In this chapter, we provide an overview of metabolomics and fluxomics, highlighting stable isotope strategies for fluxome characterization. We also discuss some of the tools used to quantitatively analyse the control exerted by components of the network over both the metabolome and the fluxome. Finally, we outline the role and future of metabolomics and fluxomics in drug discovery.

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Editorial: Omics Technologies Toward Systems Biology

Frontiers in Genetics ◽

10.3389/fgene.2021.756847 ◽

2021 ◽

Vol 12 ◽

Author(s):

Fatemeh Maghuly ◽

Gorji Marzban

Keyword(s):

Systems Biology ◽

Omics Technologies

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