Microbial cell factories have been used for the production of valuable chemical compounds using a classical metabolic engineering approach, but this requires much time and cost, and laborintensive processes to make cell factories industrially competitive. Systems metabolic engineering is an upgraded version, which understands the cell as a complex system in which networks of genes, transcripts, proteins, and metabolites are connected, facilitating the analysis of potential cell factories. However, efficient cell factory design, which aims for industrial-scale production, requires a comprehensive system, which goes beyond metabolism and considers industrial production challenges. A review is provided here of the developments and challenges in the application of systems biology for metabolic engineering and in recovery and purification processes for scaling up bio-based chemical production. Then, a new design, build, test, and learn prediction cycle for metabolic engineering is proposed, for the design of efficient cell factories. This considers system-wide characteristics and relies upon the integration of upstream (strain development), midstream (fermentation), and downstream (recovery and purification) analysis for strain design. In addition to this cycle, three issues should be taken into consideration: (i) The use of simple, available, and inexpensive materials; (ii) the identification and elimination of bottlenecks using non-complex recovery and purification processes; (iii) the assessment of commercial and chemical industry requirements from the perspective of system efficiency. In this context, highly efficient microbial cell factories should be developed to produce compounds with improved production performance to meet industrial application requirements.

BACKGROUND: Succinic acid production has been studied from a metabolic engineering or a downstream processing perspective, separately. The concentration of succinic acid and other by-products obtained after the strain design influences the production cost during the recovery and purification stage. A metabolic engineering-downstream coupling evaluation is important when selecting the metabolic targets for the strain design. In this in silico study, the metabolic engineering of an Escherichia coli strain to produce succinic acid using glycerol as a carbon source in the downstream process was evaluated in terms of operational cost and energy consumption. (0.3068, 0.0576, 0.1089 h -1 ) and succinate productivity (2.7534, 6.0772, 5.5661 mmol g -1 DW h -1 ), respectively. The results showed that the succinic acid productivity constituted a central parameter when selecting the appropriate gene targets for deletion, despite the presence of organic acids in the downstream process and the biomass growth rate. RESULTS: Three strain scenarios were selected using a bi-level linear optimization problem solved by Mixed Integer Linear Programing, and simulated in a transient fashion with dynamic flux balance analysis considering both biomass growth rate CONCLUSION: A metabolism-downstream coupled model shows that the bioproduct productivity and fermentation timeare key points when considering the operational cost and energy consumption involved in the engineering of strains for industrial-scale production.Metabolic engineering targets were obtained using OptKnock, which is a bi-level linear optimization problem solved by MILP. 33

Clostridium ( Ruminiclostridium ) thermocellum is recognized for its ability to ferment cellulosic biomass directly, but it cannot naturally grow on xylose. Recently, C. thermocellum (KJC335) was engineered to utilize xylose through expressing a heterologous xylose catabolizing pathway. Here, we compared KJC335′s transcriptomic responses to xylose versus cellobiose as the primary carbon source and assessed how the bacteria adapted to utilize xylose. Our analyses revealed 417 differentially expressed genes (DEGs) with log 2 fold change (FC) >|1| and 106 highly DEGs (log 2 FC >|2|). Among the DEGs, two putative sugar transporters, cbpC and cbpD , were up-regulated, suggesting their contribution to xylose transport and assimilation. Moreover, the up-regulation of specific transketolase genes ( tktAB ) suggests the importance of this enzyme for xylose metabolism. Results also showed remarkable up-regulation of chemotaxis and motility associated genes responding to xylose feeding, as well as widely varying gene expression in those encoding cellulosomal enzymes. For the down-regulated genes, several were categorized in gene ontology terms oxidation–reduction processes, ATP binding and ATPase activity, and integral components of the membrane. This study informs potentially critical, enabling mechanisms to realize the conceptually attractive Next-Generation Consolidated BioProcessing approach where a single species is sufficient for the co-fermentation of cellulose and hemicellulose.

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.

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The molecule (2S)-naringenin is a scaffold molecule with several nutraceutical properties. Currently, (2S)-naringenin is obtained through chemical synthesis and plant isolation. However, these methods have several drawbacks. Thus, heterologous biosynthesis has emerged as a viable alternative to its production. Recently, (2S)-naringenin production studies in Escherichia coli have used different tools to increase its yield up to 588 mg/L. In this study, we designed and assembled a bio-factory for (2S)-naringenin production. Firstly, we used several parametrized algorithms to identify the shortest pathway for producing (2S)-naringenin in E. coli, selecting the genes phenylalanine ammonia lipase (pal), 4-coumarate: CoA ligase (4cl), chalcone synthase (chs), and chalcone isomerase (chi) for the biosynthetic pathway. Then, we evaluated the effect of oxygen transfer on the production of (2S)-naringenin at flask (50 mL) and bench (4 L culture) scales. At the flask scale, the agitation rate varied between 50 rpm and 250 rpm. At the bench scale, the dissolved oxygen was kept constant at 5% DO (dissolved oxygen) and 40% DO, obtaining the highest (2S)-naringenin titer (3.11 ± 0.14 g/L). Using genome-scale modeling, gene expression analysis (RT-qPCR) of oxygen-sensitive genes was obtained.

The development of a culture medium is an essential step in any bioprocess involving microorganisms for the bioconversion of by‐products to valuable chemicals, making industries like the biofuel industry more competitive. Optimization of the bioconversion process to minimize cost while maximizing yield underscores the importance of using computational methods to identify cellular requirements under specific growth conditions. In this study, a computational approach was proposed as an alternative to optimizing glycerol consumption in one of the most common production chassis, Escherichia coli, specifically strain ATCC 8739. Nineteen compounds were identified as essential for E. coli growth in glycerol. Of these, three reactions associated with nitrogen, phosphorous, and oxygen availability were determined as crucial to reaching high growth and glycerol uptake rates. Based on computational results, a glycerol‐based medium was supplemented with reported common chemical compounds that contain nitrogen or phosphorous (NH4Cl, Na2HPO4, and K2HPO4) for further experimental validation. When comparing the supplemented culture medium experimentally with LB medium (Luria Bertani), a two‐fold increment in the glycerol consumption was observed. Transcriptomic analysis of the most promising culture medium reveals that high glycerol utilization under aerobic conditions is dependent on phosphorus to avoid toxicity within the cell because of glycerol‐3‐phosphate generation. The result of this study is a resource to determine nutritional requirements that allow the improvement of the use of raw material for the production of compounds that are attractive to the bio‐based industry. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

BackgroundGlycerol has become an interesting carbon source for industrial processes as consequence of the biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. Selecting the appropriate metabolic targets to build efficient cell factories and maximize the desired chemical production in as little time as possible is a major challenge in industrial biotechnology. The engineering of microbial metabolism following rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, to be proficient is needed known in advance the effects of gene deletions.ResultsAn in silico experiment was performed to model and understand the effects of metabolic engineering over the metabolism by transcriptomics data integration. In this study, systems-based metabolic engineering to predict the metabolic engineering targets was used in order to increase the bioconversion of glycerol to succinic acid by Escherichia coli. Transcriptomics analysis suggest insights of how increase the glycerol utilization of the cell for further design efficient cell factories. Three models were used; an E. coli core model, a model obtained after the integration of transcriptomics data obtained from E. coli growing in an optimized culture media, and a third one obtained after integration of transcriptomics data obtained from E. coli after adaptive laboratory evolution experiments. A total of 2402 strains were obtained from these three models. Fumarase and pyruvate dehydrogenase were frequently predicted in all the models, suggesting that these reactions are essential to increasing succinic acid production from glycerol. Finally, 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 importance of each knockout’s (feature’s) contribution.ConclusionsThe combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed versatile molecular mechanisms involved in the utilization of glycerol. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work a guide platform for the selection/engineering of microorganisms for production of interesting chemical compounds.