Multiomics data collection, visualization, and utilization for guiding metabolic engineering

Abstract

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we present a set of tools that, combined, provide the ability to store, visualize and leverage these data to predict the outcome of bioengineering efforts.

OMICs-Based Strategies to Explore Stress Tolerance Mechanisms of Saccharomyces cerevisiae for Efficient Fuel Ethanol Production

“…The regularly intrinsic metabolism pathways can be optimized or directedly altered via inverse metabolic engineering (IME) for efficiently producing desired phenotypes, which a systematic process including 1) choosing the target genes, 2) construction of engineered strain, and 3) high-throughput screening and finetuning on strain breeding (Mehmood et al, 2017b;Pereira et al, 2021). Combined with multi-OMICs data analyses, genetic manipulation has been successfully applied in overexpressing decisive genes affecting metabolic flow, gene deletion, or mutation for blocking and alleviating the competing pathway, even heterologous expression (Liu et al, 2013;Chen and Dou, 2016;Roy et al, 2020); additionally, some approaches such as global transcription machinery engineering (gTME) and multiplex automated genome engineering (MAGE) could be selectively utilized to support the IME for increased alcohol fermentation and stress tolerance (Liu and Jiang, 2015;Adebami et al, 2021). Promoted alcohol tolerance is elicited by overexpressing indigenous genes INO1 (encoding an inositol-3-phosphate synthase), HAL1 (encoding a cytoplasmic protein), and DOG1 (encoding 2-deoxyglucose-6-phosphate phosphatase involved in glucose metabolism) of S. cerevisiae or a truncated form of MSN2 in CEN.…”