Synthesizing Systems Biology Knowledge from Omics Using Genome‐Scale Models. Issue 17 (12th July 2020)

• Record Type: Journal Article
• Authors: Dahal, Sanjeev; Yurkovich, James T.; Xu, Hao; Palsson, Bernhard O.; Yang, Laurence
• Is Part Of: Proteomics. Volume 20:Issue 17/18(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-12
• Subjects: computational model -- genome‐scale model -- genomics -- machine learning -- systems biology; Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201900282
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract: Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome‐scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi‐omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome‐scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta‐omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome‐scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.