Synthesizing Systems Biology Knowledge from Omics Using Genome‐Scale Models

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Dahal, Sanjeev
Yurkovich, James
Xu, Hao
Palsson, Bernhard
Yang, Laurence
Systems Biology , Genome‐Scale Model , Computational Model , Machine Learning , Genomics
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, we focus on genome‐scale models (GEMs) 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. We review a variety of genome‐scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta‐omics. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. We find that concurrent with advancements in omic technologies, genome‐scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, we expect continued synthesis of valuable knowledge through the integration of omic data with GEMs.