In silico Design for Systems-Based Metabolic Engineering for the Bioconversion of Valuable Compounds From Industrial By-Products

Abstract

Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a 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 that allow predicting 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, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to 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 (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) 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 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 and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

Minireview: Engineering evolution to reconfigure phenotypic traits in microbes for biotechnological applications

“…Integration of the RNA-Seq data that reflect the transcriptome state of 2,3-BDO-producing conditions into the GEM enabled in silico 2,3-BDO production, which closely aligned with the experimental results [83] . These findings demonstrate the robustness of the omics-integrated GEM framework in reflecting condition-specific metabolic states and the extended utility of systems metabolic engineering approaches (encompassing GEM, Omics, and ALE) in harnessing highly efficient production strains [20] .…”

“…2 C). Initially applied in E. coli for the overproduction of native and heterologous compounds [15] , [16] , [17] , [18] , [19] , [20] , [70] , [73] , [74] , [75] , [76] , this approach now extends to a diverse array of “GEM-accessible” microorganisms [77] . Evolutionary engineering is increasingly implemented in tandem with GEMs to enhance the target product titer, largely by optimizing cellular metabolism or compensating for fitness defects that would result from metabolic engineering.…”

“…The computational framework of cellular metabolism, known as the genome-scale metabolic model (GEM), provides for a highly accurate description and interpretation of metabolic phenotypes of adaptively evolved strains [13] , [14] . As a result, various GEM-derived systems biology frameworks have been used together with the ALE techniques to improve target compound production [15] , [16] , [17] , [18] , [19] , [20] and acquire complex phenotypes [21] , [22] . Thus, ALE demonstrates extended utility in strain engineering and serves a tool that informs users of the best possible optimization scenarios.…”

Optimizing the Production of Valuable Metabolites using a Hybrid of Constraint-based Model and Machine Learning Algorithms: A Review

“…Machine Learning (ML) has been increasingly used in metabolic engineering to replace human metabolic engineers [33], [34], [35]. Given its success in pattern recognition, model prediction, and others [36], [37], [38], [39], [40].…”

“…ML has recently played a significant role in biological research [16], [39]. These algorithms focus on model performance by training highly heterogeneous data.…”