4. Incorporation of constraint-based modeling and machine learning
In recent years, the advances in high-throughput devices and the rapid growth of omics data provide a unique opportunity to depict biological samples at multiple layers. Omics data generated from high-throughput technologies are big data that can be analyzed individually or inferred as multi-omic relationships through ML algorithms to gain more biological insight [109-111]. Common omics datasets include genomics, transcriptomics, proteomics, and metabolomics produced from DNA sequencing, RNA sequencing and microarrays, and mass spectrometry, respectively [112]. Furthermore, fluxomics is an additional layer of omics generated from CBM approaches and includes metabolic flux distribution values, thus representing the metabolic phenotype [113]. Consequently, the integration of ML (for omics and multi-omics analysis) and CBM (for generating fluxomics) looks promising for analyzing a biological system such as cellular metabolism. However, the capabilities of this hybrid approach are just now being explored, and more research in this area is needed. In this section, first, we briefly summarize the ways that ML and CBM can be coupled. Next, the most prominent studies aiming at analysis and optimization of fermentation parameters by ML-CBM approaches are reviewed.