Machine and deep learning meet genome-scale metabolic modeling

Today, the search for biological mechanisms at molecular scale can leverage an unprecedented amount of information. With the recent development of high-throughput technologies, data collection has received an enormous impulse that has radically changed the perspective toward molecular biology. The main protagonist of this shift is omic data--namely, experimental profiles with large coverage over multiple biological domains. Several levels of knowledge have become associated with emerging omic technologies [1–3]. The most widespread to date include DNA sequencing (genomics), microarrays and RNA sequencing (transcriptomics), DNA methylation and histone modifications (epigenomics), and protein or metabolite mass spectrometry (proteomics and metabolomics). As technology moves forward, its associated costs decrease, and a growing wealth of data is being generated.