Synthesizing Systems Biology Knowledge from Omics Using Genome‐Scale Models

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.

Integration strategies of multi-omics data for machine learning analysis

“…Clinical information and omics data can be directly retrieved from databases or collected with screening technologies for disease [6] , class prediction [7] , biomarkers discovery [8] , disease subtyping [6] , improved system biology knowledge [9] , drug repurposing and so on. Each type of omics data is specific to a single “layer” of biological information such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and provides a complementary medical perspective of a biological system or an individual [1] .…”

Water as a reactant in the differential expression of proteins in cancer

“…The biosynthesis of each type of nonessential amino acid entails different stoichiometric requirements for the amounts of biochemical precursors (e.g., 15, 16), and it follows that proteins with different amino acid composition also have different precursor requirements. However, a reported genome‐scale metabolic model for cancer cells represents cellular proteins with a single average amino acid composition 17, while metabolic models that can be used to predict macromolecular expression levels (ME‐models) are currently available only for bacterial cells 18. Because of this limitation, bottom‐up approaches toward quantifying the metabolic requirements for changes in protein abundance at the proteome scale in cancer are at present a daunting computational challenge.…”

Synergies of Systems Biology and Synthetic Biology in Human Microbiome Studies

“…For instance, the COBRA (Constraint-Based Reconstruction and Analysis) toolbox contains a function that integrates modeling of experimental molecular systems biology data and enables the prediction of, for instance, phenotypic properties at a genome scale ( Heirendt et al, 2007 ). Mathematical models in biology are a useful platform for either the integration of omics data for new discoveries or to perform simulations to generate new hypotheses ( Dahal et al, 2020 ). There are different types of mathematical modeling approaches such as differential equation models, dynamic models, and constraint-based stoichiometric models which provide insights into the functioning of the microbiome ( Zomorrodi and Segrè, 2016 ).…”