Large-scale bi-level strain design approaches and mixed-integer programming solution techniques

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ～10 days to ～5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.     

Engineering antibiotic production and overcoming bacterial resistance

Progress in DNA technology, analytical methods and computational tools is leading to new developments in synthetic biology and metabolic engineering, enabling new ways to produce molecules of industrial and therapeutic interest. Here, we review recent progress in both antibiotic production and strategies to counteract bacterial resistance to antibiotics. Advances in sequencing and cloning are increasingly enabling the characterization of antibiotic biosynthesis pathways, and new systematic methods for de novo biosynthetic pathway prediction are allowing the exploration of the metabolic chemical space beyond metabolic engineering. Moreover, we survey the computer-assisted design of modular assembly lines in polyketide synthases and non-ribosomal peptide synthases for the development of tailor-made antibiotics. Nowadays, production of novel antibiotic can be tranferred into any chosen chassis by optimizing a host factory through specific strain modifications. These advances in metabolic engineering and synthetic biology are leading to novel strategies for engineering antimicrobial agents with desired specificities.     

Systems metabolic engineering design: Fatty acid production as an emerging case study

Increasing demand for petroleum has stimulated industry to develop sustainable production of chemicals and biofuels using microbial cell factories. Fatty acids of chain lengths from C₆ to C₁₆ are propitious intermediates for the catalytic synthesis of industrial chemicals and diesel‐like biofuels. The abundance of genetic information available for Escherichia coli and specifically, fatty acid metabolism in E. coli, supports this bacterium as a promising host for engineering a biocatalyst for the microbial production of fatty acids. Recent successes rooted in different features of systems metabolic engineering in the strain design of high‐yielding medium chain fatty acid producing E. coli strains provide an emerging case study of design methods for effective strain design. Classical metabolic engineering and synthetic biology approaches enabled different and distinct design paths towards a high‐yielding strain. Here we highlight a rational strain design process in systems biology, an integrated computational and experimental approach for carboxylic acid production, as an alternative method. Additional challenges inherent in achieving an optimal strain for commercialization of medium chain‐length fatty acids will likely require a collection of strategies from systems metabolic engineering. Not only will the continued advancement in systems metabolic engineering result in these highly productive strains more quickly, this knowledge will extend more rapidly the carboxylic acid platform to the microbial production of carboxylic acids with alternate chain‐lengths and functionalities. Biotechnol. Biotechnol. Bioeng. 2014;111: 849–857. © 2014 Wiley Periodicals, Inc.     

Overexpression of O‐methyltransferase leads to improved vanillin production in baker's yeast only when complemented with model‐guided network engineering

Overproduction of a desired metabolite is often achieved via manipulation of the pathway directly leading to the product or through engineering of distant nodes within the metabolic network. Empirical examples illustrating the combined effect of these local and global strategies have been so far limited in eukaryotic systems. In this study, we compared the effects of overexpressing a key gene in de novo vanillin biosynthesis (coding for O‐methyltransferase, hsOMT) in two yeast strains, with and without model‐guided global network modifications. Overexpression of hsOMT resulted in increased vanillin production only in the strain with model‐guided modifications, exemplifying advantage of using a global strategy prior to local pathway manipulation. Biotechnol. Bioeng. 2013; 110: 656–659. © 2012 Wiley Periodicals, Inc.     

A systematic analysis of TCA Escherichia coli mutants reveals suitable genetic backgrounds for enhanced hydrogen and ethanol production using glycerol as main carbon source

Biodiesel has emerged as an environmentally friendly alternative to fossil fuels; however, the low price of glycerol feed‐stocks generated from the biodiesel industry has become a burden to this industry. A feasible alternative is the microbial biotransformation of waste glycerol to hydrogen and ethanol. Escherichia coli, a microorganism commonly used for metabolic engineering, is able to biotransform glycerol into these products. Nevertheless, the wild type strain yields can be improved by rewiring the carbon flux to the desired products by genetic engineering. Due to the importance of the central carbon metabolism in hydrogen and ethanol synthesis, E. coli single null mutant strains for enzymes of the TCA cycle and other related reactions were studied in this work. These strains were grown anaerobically in a glycerol‐based medium and the concentrations of ethanol, glycerol, succinate and hydrogen were analysed by HPLC and GC. It was found that the reductive branch is the more relevant pathway for the aim of this work, with malate playing a central role. It was also found that the putative C4‐transporter dcuD mutant improved the target product yields. These results will contribute to reveal novel metabolic engineering strategies for improving hydrogen and ethanol production by E. coli.     

Natural computation meta-heuristics for the in silico optimization of microbial strains

Background  

One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for in silico metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.     

Genome-scale metabolic models as platforms for strain design and biological discovery

Genome-scale metabolic models (GEMs) have been developed and used in guiding systems’ metabolic engineering strategies for strain design and development. This strategy has been used in fermentative production of bio-based industrial chemicals and fuels from alternative carbon sources. However, computer-aided hypotheses building using established algorithms and software platforms for biological discovery can be integrated into the pipeline for strain design strategy to create superior strains of microorganisms for targeted biosynthetic goals. Here, I described an integrated workflow strategy using GEMs for strain design and biological discovery. Specific case studies of strain design and biological discovery using Escherichia coli genome-scale model are presented and discussed. The integrated workflow presented herein, when applied carefully would help guide future design strategies for high-performance microbial strains that have existing and forthcoming genome-scale metabolic models.     

Heterologous production of resveratrol in bacterial hosts: current status and perspectives

The polyphenol resveratrol (3,5,4′-trihydroxystilbene) is a well-known plant secondary metabolite, commonly used as a medical ingredient and a nutritional supplement. Due to its health-promoting properties, the demand for resveratrol is expected to continue growing. This stilbene can be found in different plants, including grapes, berries (blackberries, blueberries and raspberries), peanuts and their derived food products, such as wine and juice. The commercially available resveratrol is usually extracted from plants, however this procedure has several drawbacks such as low concentration of the product of interest, seasonal variation, risk of plant diseases and product stability. Alternative production processes are being developed to enable the biotechnological production of resveratrol by genetically engineering several microbial hosts, such as Escherichia coli, Corynebacterium glutamicum, Lactococcus lactis, among others. However, these bacterial species are not able to naturally synthetize resveratrol and therefore genetic modifications have been performed. The application of emerging metabolic engineering offers new possibilities for strain and process optimization. This mini-review will discuss the recent progress on resveratrol biosynthesis in engineered bacteria, with a special focus on the metabolic engineering modifications, as well as the optimization of the production process. These strategies offer new tools to overcome the limitations and challenges for microbial production of resveratrol in industry.     

Genetic improvement of brewer’s yeast: current state, perspectives and limits

Brewer’s yeast strain optimisation may lead to a more efficient beer production process, better final quality or healthier beer. However, brewer’s yeast genetic improvement is very challenging, especially true when it comes to lager brewer’s yeast (Saccharomyces pastorianus) which contributes to 90% of the total beer market. This yeast is a genetic hybrid and allopolyploid. While early studies applying traditional genetic approaches encountered many problems, the development of rational metabolic engineering strategies successfully introduced many desired properties into brewer’s yeast. Recently, the first genome sequence of a lager brewer’s strain became available. This has opened the door for applying advanced omics technologies and facilitating inverse metabolic engineering strategies. The latter approach takes advantage of natural diversity and aims at identifying and transferring the crucial genetic information for an interesting phenotype. In this way, strains can be optimised by introducing “natural” mutations. However, even when it comes to self-cloned strains, severe concerns about genetically modified organisms used in the food and beverage industry are still a major hurdle for any commercialisation. Therefore, research efforts will aim at developing new sophisticated screening methods for the isolation of natural mutants with the desired properties which are based on the knowledge of genotype–phenotype linkage.     

Microbial production of lactate-containing polyesters

Due to our increasing concerns on environmental problems and limited fossil resources, biobased production of chemicals and materials through biorefinery has been attracting much attention. Optimization of the metabolic performance of microorganisms, the key biocatalysts for the efficient production of the desired target bioproducts, has been achieved by metabolic engineering. Metabolic engineering allowed more efficient production of polyhydroxyalkanoates, a family of microbial polyesters. More recently, non-natural polyesters containing lactate as a monomer have also been produced by one-step fermentation of engineered bacteria. Systems metabolic engineering integrating traditional metabolic engineering with systems biology, synthetic biology, protein/enzyme engineering through directed evolution and structural design, and evolutionary engineering, enabled microorganisms to efficiently produce natural and non-natural products. Here, we review the strategies for the metabolic engineering of microorganisms for the in vivo biosynthesis of lactate-containing polyesters and for the optimization of whole cell metabolism to efficiently produce lactate-containing polyesters. Also, major problems to be solved to further enhance the production of lactate-containing polyesters are discussed.     

CRISPR interference–mediated metabolic engineering of Corynebacterium glutamicum for homo‐butyrate production

Combinatorial metabolic engineering enabled the development of efficient microbial cell factories for modulating gene expression to produce desired products. Here, we report the combinatorial metabolic engineering of Corynebacterium glutamicum to produce butyrate by introducing a synthetic butyrate pathway including phosphotransferase and butyrate kinase reactions and repressing the essential acn gene‐encoding aconitase, which has been targeted for downregulation in a genome‐scale model. An all‐in‐one clustered regularly interspaced short palindromic repeats interference system for C. glutamicum was used for tunable downregulation of acn in an engineered strain, where by‐product‐forming reactions were deleted and the synthetic butyrate pathway was inserted, resulting in butyrate production (0.52 ± 0.02 g/L). Subsequently, biotin limitation enabled the engineered strain to produce butyrate (0.58 ± 0.01 g/L) without acetate formation for the entire duration of the culture. These results demonstrate the potential homo‐production of butyrate using engineered C. glutamicum. This method can also be applied to other industrial microorganisms.     

Deriving metabolic engineering strategies from genome-scale modeling with flux ratio constraints

Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H₂ production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design “fine-tuned” metabolic engineering strategies in silico that can be implemented directly with available genomic tools.     

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.     

Escherichia coli genome-scale metabolic gene knockout of lactate dehydrogenase (ldhA), increases succinate production from glycerol

Genome-scale metabolic model (GEM) of Escherichia coli has been published with applications in predicting metabolic engineering capabilities on different carbon sources and directing biological discovery. The use of glycerol as an alternative carbon source is economically viable in biorefinery. The use of GEM for predicting metabolic gene deletion of lactate dehydrogenase (ldhA) for increasing succinate production in Escherichia coli from glycerol carbon source remained largely unexplored. Here, I hypothesized that metabolic gene knockout of ldhA in E. coli from glycerol could increase succinate production. A proof-of-principle strain was constructed and designated as E. coli BMS5 (ΔldhA), by predicting increased succinate production in E. coli GEM and confirmed the predicted outcomes using wet cell experiments. The mutant GEM (ΔldhA) predicted 11% increase in succinate production from glycerol compared to its wild-type model (iAF1260), and the E. coli BMS5 (ΔldhA) showed 1.05 g/l and its corresponding wild-type produced .05 g/l (23-fold increase). The proof-of-principle strain constructed in this study confirmed the aforementioned hypothesis and further elucidated the fact that E. coli GEM can prospectively and effectively predict metabolic engineering interventions using glycerol as substrate and could serve as platform for new strain design strategies and biological discovery.     

OptORF: Optimal metabolic and regulatory perturbations for metabolic engineering of microbial strains

Background  

Computational modeling and analysis of metabolic networks has been successful in metabolic engineering of microbial strains for valuable biochemical production. Limitations of currently available computational methods for metabolic engineering are that they are often based on reaction deletions rather than gene deletions and do not consider the regulatory networks that control metabolism. Due to the presence of multi-functional enzymes and isozymes, computational designs based on reaction deletions can sometimes result in strategies that are genetically complicated or infeasible. Additionally, strains might not be able to grow initially due to regulatory restrictions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression.     

Construction of a Genome-Scale Metabolic Model of Arthrospira platensis NIES-39 and Metabolic Design for Cyanobacterial Bioproduction

Arthrospira (Spirulina) platensis is a promising feedstock and host strain for bioproduction because of its high accumulation of glycogen and superior characteristics for industrial production. Metabolic simulation using a genome-scale metabolic model and flux balance analysis is a powerful method that can be used to design metabolic engineering strategies for the improvement of target molecule production. In this study, we constructed a genome-scale metabolic model of A. platensis NIES-39 including 746 metabolic reactions and 673 metabolites, and developed novel strategies to improve the production of valuable metabolites, such as glycogen and ethanol. The simulation results obtained using the metabolic model showed high consistency with experimental results for growth rates under several trophic conditions and growth capabilities on various organic substrates. The metabolic model was further applied to design a metabolic network to improve the autotrophic production of glycogen and ethanol. Decreased flux of reactions related to the TCA cycle and phosphoenolpyruvate reaction were found to improve glycogen production. Furthermore, in silico knockout simulation indicated that deletion of genes related to the respiratory chain, such as NAD(P)H dehydrogenase and cytochrome-c oxidase, could enhance ethanol production by using ammonium as a nitrogen source.     

Model-driven evaluation of the production potential for growth-coupled products of Escherichia coli

Integrated approaches utilizing in silico analyses will be necessary to successfully advance the field of metabolic engineering. Here, we present an integrated approach through a systematic model-driven evaluation of the production potential for the bacterial production organism Escherichia coli to produce multiple native products from different representative feedstocks through coupling metabolite production to growth rate. Designs were examined for 11 unique central metabolism and amino acid targets from three different substrates under aerobic and anaerobic conditions. Optimal strain designs were reported for designs which possess maximum yield, substrate-specific productivity, and strength of growth-coupling for up to 10 reaction eliminations (knockouts). In total, growth-coupled designs could be identified for 36 out of the total 54 conditions tested, corresponding to eight out of the 11 targets. There were 17 different substrate/target pairs for which over 80% of the theoretical maximum potential could be achieved. The developed method introduces a new concept of objective function tilting for strain design. This study provides specific metabolic interventions (strain designs) for production strains that can be experimentally implemented, characterizes the potential for E. coli to produce native compounds, and outlines a strain design pipeline that can be utilized to design production strains for additional organisms.     

Computational design of auxotrophy-dependent microbial biosensors for combinatorial metabolic engineering experiments

Combinatorial approaches in metabolic engineering work by generating genetic diversity in a microbial population followed by screening for strains with improved phenotypes. One of the most common goals in this field is the generation of a high rate chemical producing strain. A major hurdle with this approach is that many chemicals do not have easy to recognize attributes, making their screening expensive and time consuming. To address this problem, it was previously suggested to use microbial biosensors to facilitate the detection and quantification of chemicals of interest. Here, we present novel computational methods to: (i) rationally design microbial biosensors for chemicals of interest based on substrate auxotrophy that would enable their high-throughput screening; (ii) predict engineering strategies for coupling the synthesis of a chemical of interest with the production of a proxy metabolite for which high-throughput screening is possible via a designed bio-sensor. The biosensor design method is validated based on known genetic modifications in an array of E. coli strains auxotrophic to various amino-acids. Predicted chemical production rates achievable via the biosensor-based approach are shown to potentially improve upon those predicted by current rational strain design approaches. (A Matlab implementation of the biosensor design method is available via http://www.cs.technion.ac.il/~tomersh/tools).     

Towards a metabolic engineering strain “commons”: An Escherichia coli platform strain for ethanol production

In the genome‐engineering era, it is increasingly important that researchers have access to a common set of platform strains that can serve as debugged production chassis and the basis for applying new metabolic engineering strategies for modeling and characterizing flux, engineering complex traits, and optimizing overall performance. Here, we describe such a platform strain of E. coli engineered for ethanol production. Starting with a fully characterized host strain (BW25113), we site‐specifically integrated the genes required for homoethanol production under the control of a strong inducible promoter into the genome and deleted the genes encoding four enzymes from competing pathways. This strain is capable of producing >30 g/L of ethanol in minimal media with <2 g/L produced of any fermentative byproduct. Using this platform strain, we tested previously identified ethanol tolerance genes and found that while tolerance was improved under certain conditions, any effect on ethanol production or tolerance was lost when grown under production conditions. Thus, our findings reinforce the need for a metabolic engineering “commons” that could provide a set of platform strains for use in more sophisticated genome‐engineering strategies. Towards this end, we have made this production strain available to the scientific community. Biotechnol. Bioeng. 2013; 110: 1520–1526. © 2013 Wiley Periodicals, Inc.