In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products - Tafur Rangel, Albert Enrique; Ríos, Wendy; Mejía, Daisy; Ojeda, Carmen; Carlson, Ross; Gómez Ramírez, Jorge Mario; González Barrios, Andrés Fernando (Frontiers in Genetics, 2021)
Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the industrial by-products bioconversion to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. The microbial metabolism engineering following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools aimed to allow to predict gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based in Escherichia coli. Transcriptomics analysis suggests insights on how to increase the cell glycerol utilization for further design efficient cell factories. Three E. coli models were used; a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution experiments. A total of 2402 strains were obtained with fumarase and pyruvate dehydrogenase where frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout’s (feature’s) contribution. Glycerol has become an interesting carbon source for industrial processes due to the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.