id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-289447-d93qwjui	Helmy, Mohamed	Systems biology approaches integrated with artificial intelligence for optimized food-focused metabolic engineering	2020-10-09		.txt	text/plain	7405	359	38	Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms. On the other hand, Jervis et al implemented an ML algorithm to model the bacterial ribosome binding sites (RBSs) sequence-phenotype relationship and accurately predicted the optimal high-producers, an approach that directly apply on wide range of metabolic engineering applications [106] . To understand the key regulatory or emergent bottleneck scenarios that limit their industrial applicability, they undertook a large scale -omics based systems biology approach where they performed time-series proteomics and metabolomics measurements, and analyzed the resultant high-throughput data using statistical analytics and genome-scale modeling. Although genome annotation, both structural and functional, affects most of the biomedical research aspects, it has a special impact on metabolic engineering in general and applications in food industry in particular.	./cache/cord-289447-d93qwjui.txt	./txt/cord-289447-d93qwjui.txt