Unveiling the potential of systems biology in biotechnology and biomedical research

Abstract

“In silico organisms” are computational genome-scale metabolic models used in systems and synthetic biology developed by constraint-based metabolic simulations using multi-omics and phenotypic data. The quality of these models is hidden because of the limited availability of genomic information and genome-scale metabolic reconstruction methods. In this review, 237 manually curated genome-scale models for various organisms with industrial and clinical significance were comprehensively reviewed, and their modelling information was tabulated based on literature. This review provides a comprehensive summary of potential applications of systems biology in biotechnology and biomedical research. Their broad applicability has been explored in the process of model improvement and design of experiments in metabolic design and drug development. This review summarizes their recent advances, challenges, and practical applications in Gram-negative bacteria, Gram-positive bacteria, archaea, fungi, algae, plants, and animals. Genome-scale models of microbes have been reviewed to address their various applications in metabolic systems engineering, strain optimization, bioremediation, biomanufacturing, and personalized systems medicine. Several models have been explored to understand the molecular mechanisms underlying pathogenesis, virulence, host-microbe interactions, and metabolic crosstalk. This review provides an overview of the current knowledge on human metabolic reconstructions and their important roles in human, microbiota-related, and complex metabolic disorders. Genome-scale models of human and animal metals offer ethical alternatives to the traditional animal testing methods. Current progress in systems biology research will lead to the development of indispensable databases, computational tools, and analytical platforms. This will strengthen data-driven discovery and facilitate integration of biological information into living systems.

Keywords

Systems biology / Genome-scale models / In silico organisms / Constraint-based modeling / Cell factory / Synthetic biology

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Funding

Science and Engineering Research Board Government of India(EEQ/2020/000095)