scholarly journals Omics-Driven Biotechnology for Industrial Applications

2021 ◽  
Vol 9 ◽  
Author(s):  
Bashar Amer ◽  
Edward E. K. Baidoo
Keyword(s):  
Metabolic Engineering ◽  
Systems Biology ◽  
Synthetic Biology ◽  
Metabolic Networks ◽  
Industrial Applications ◽  
Omics Technologies ◽  
Food Biotechnology ◽  
Food And Agriculture ◽  
Pros And Cons ◽  
The Impact

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.

scholarly journals Current Status and Future Strategies to Increase Secondary Metabolite Production from Cyanobacteria

2020 ◽  
Vol 8 (12) ◽  
pp. 1849
Author(s):  
Yujin Jeong ◽  
Sang-Hyeok Cho ◽  
Hookeun Lee ◽  
Hyung-Kyoon Choi ◽  
Dong-Myung Kim ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Systems Biology ◽  
Secondary Metabolites ◽  
Regulatory Networks ◽  
Carbon Fixation ◽  
Industrial Applications ◽  
Current Status ◽  
Enhanced Production ◽  
Biochemical Production ◽  
Genome Scale

Cyanobacteria, given their ability to produce various secondary metabolites utilizing solar energy and carbon dioxide, are a potential platform for sustainable production of biochemicals. Until now, conventional metabolic engineering approaches have been applied to various cyanobacterial species for enhanced production of industrially valued compounds, including secondary metabolites and non-natural biochemicals. However, the shortage of understanding of cyanobacterial metabolic and regulatory networks for atmospheric carbon fixation to biochemical production and the lack of available engineering tools limit the potential of cyanobacteria for industrial applications. Recently, to overcome the limitations, synthetic biology tools and systems biology approaches such as genome-scale modeling based on diverse omics data have been applied to cyanobacteria. This review covers the synthetic and systems biology approaches for advanced metabolic engineering of cyanobacteria.

scholarly journals Synthetic Biology Open Language (SBOL) Version 2.3

2019 ◽  
Vol 16 (2) ◽  
Author(s):  
Curtis Madsen ◽  
Angel Goñi Moreno ◽  
Umesh P ◽  
Zachary Palchick ◽  
Nicholas Roehner ◽  
...  
Keyword(s):  
Experimental Data ◽  
Metabolic Engineering ◽  
Systems Biology ◽  
Synthetic Biology ◽  
Molecular Biology ◽  
Special Issue ◽  
Design Elements ◽  
Biological Design ◽  
Support Organization ◽  
Synthetic Biology Open Language

AbstractSynthetic biology builds upon the techniques and successes of genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. The field still faces substantial challenges, including long development times, high rates of failure, and poor reproducibility. One method to ameliorate these problems is to improve the exchange of information about designed systems between laboratories. The synthetic biology open language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, filling a need not satisfied by other pre-existing standards. This document details version 2.3.0 of SBOL, which builds upon version 2.2.0 published in last year’s JIB Standards in Systems Biology special issue. In particular, SBOL 2.3.0 includes means of succinctly representing sequence modifications, such as insertion, deletion, and replacement, an extension to support organization and attachment of experimental data derived from designs, and an extension for describing numerical parameters of design elements. The new version also includes specifying types of synthetic biology activities, unambiguous locations for sequences with multiple encodings, refinement of a number of validation rules, improved figures and examples, and clarification on a number of issues related to the use of external ontology terms.

scholarly journals Article Commentary: Promise and Reality in the Expanding Field of Network Interaction Analysis: Metabolic Networks

2014 ◽  
Vol 8 ◽  
pp. BBI.S12466 ◽  
Author(s):  
Susanna Bazzani
Keyword(s):  
Metabolic Engineering ◽  
Systems Biology ◽  
Metabolic Networks ◽  
Interaction Analysis ◽  
Cellular Metabolism ◽  
Research Fields ◽  
Network Interaction ◽  
Biological Phenomena ◽  
Pharmacology Biochemistry ◽  
New Algorithms

In the last few decades, metabolic networks revealed their capabilities as powerful tools to analyze the cellular metabolism. Many research fields (eg, metabolic engineering, diagnostic medicine, pharmacology, biochemistry, biology and physiology) improved the understanding of the cell combining experimental assays and metabolic network-based computations. This process led to the rise of the “systems biology” approach, where the theory meets experiments and where two complementary perspectives cooperate in the study of biological phenomena. Here, the reconstruction of metabolic networks is presented, along with established and new algorithms to improve the description of cellular metabolism. Then, advantages and limitations of modeling algorithms and network reconstruction are discussed.

Metabolomics and fluxomics approaches

2008 ◽  
Vol 45 ◽  
pp. 67-82 ◽  
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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