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There have been a growing number of computational strategies to aid in the design of synthetic microbial consortia. A framework to identify regions in parametric space to maximize two essential properties, evenness and stability, is critical. In this study, we introduce DyMMM-LEAPS (dynamic multispecies metabolic modeling-locating evenness and stability in large parametric space), an extension of the DyMMM framework. Our method explores the large parametric space of genetic circuits in synthetic microbial communities to identify regions of evenness and stability. Due to the high computational costs of exhaustive sampling, we utilize adaptive sampling and surrogate modeling to reduce the number of simulations required to map the vast space. Our framework predicts engineering targets and computes their operating ranges to maximize the probability of the engineered community to have high evenness and stability. We demonstrate our approach by simulating five cocultures and one three-strain culture with different social interactions (cooperation, competition, and predation) employing quorum-sensing-based genetic circuits. In addition to guiding circuit tuning, our pipeline gives an opportunity for a detailed analysis of pockets of evenness and stability for the circuit under investigation, which can further help dissect the relationship between the two properties. DyMMM-LEAPS is easily customizable and can be expanded to a larger community with more complex interactions.
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Chloroform (CF) and dichloromethane (DCM) are groundwater contaminants of concern due to their high toxicity and inhibition of important biogeochemical processes such as methanogenesis. Anaerobic biotransformation of CF and DCM has been well documented but typically independently of one another. CF is the electron acceptor for certain organohalide-respiring bacteria that use reductive dehalogenases (RDases) to dechlorinate CF to DCM. In contrast, known DCM degraders use DCM as their electron donor, which is oxidized using a series of methyltransferases and associated proteins encoded by the mec cassette to facilitate the entry of DCM to the Wood-Ljungdahl pathway. The SC05 culture is an enrichment culture sold commercially for bioaugmentation, which transforms CF via DCM to CO2. This culture has the unique ability to dechlorinate CF to DCM using electron equivalents provided by the oxidation of DCM to CO2. Here, we use metagenomic and metaproteomic analyses to identify the functional genes involved in each of these transformations. Though 91 metagenome-assembled genomes were assembled, the genes for an RDase-named acdA-and a complete mec cassette were found to be encoded on a single contig belonging to Dehalobacter. AcdA and critical Mec proteins were also highly expressed by the culture. Heterologously expressed AcdA dechlorinated CF and other chloroalkanes but had 100-fold lower activity on DCM. Overall, the high expression of Mec proteins and the activity of AcdA suggest a Dehalobacter capable of dechlorination of CF to DCM and subsequent mineralization of DCM using the mec cassette. IMPORTANCE: Chloroform (CF) and dichloromethane (DCM) are regulated groundwater contaminants. A cost-effective approach to remove these pollutants from contaminated groundwater is to employ microbes that transform CF and DCM as part of their metabolism, thus depleting the contamination as the microbes continue to grow. In this work, we investigate bioaugmentation culture SC05, a mixed microbial consortium that effectively and simultaneously degrades both CF and DCM coupled to the growth of Dehalobacter. We identified the functional genes responsible for the transformation of CF and DCM in SC05. These genetic biomarkers provide a means to monitor the remediation process in the field.
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Proteínas de Bactérias , Clorofórmio , Cloreto de Metileno , Consórcios Microbianos , Clorofórmio/metabolismo , Cloreto de Metileno/metabolismo , Consórcios Microbianos/genética , Proteínas de Bactérias/genética , Proteínas de Bactérias/metabolismo , Biodegradação Ambiental , Água Subterrânea/microbiologia , Metagenômica , Poluentes Químicos da Água/metabolismoRESUMO
Chain elongating bacteria are a unique guild of strictly anaerobic bacteria that have garnered interest for sustainable chemical manufacturing from carbon-rich wet and gaseous waste streams. They produce C6-C8 medium-chain fatty acids which are valuable platform chemicals that can be used directly, or derivatized to service a wide range of chemical industries. However, the application of chain elongating bacteria for synthesizing products beyond C6-C8 medium-chain fatty acids has not been evaluated. In this study, we assess the feasibility of expanding the product spectrum of chain elongating bacteria to C9-C12 fatty acids, along with the synthesis of C6 fatty alcohols, dicarboxylic acids, diols, and methyl ketones. We propose several metabolic engineering strategies to accomplish these conversions in chain elongating bacteria and utilize constraint-based metabolic modelling to predict pathway stoichiometries, assess thermodynamic feasibility, and estimate ATP and product yields. We also evaluate how producing alternative products impacts the growth rate of chain elongating bacteria via resource allocation modelling, revealing a trade-off between product carbon length and class versus cell growth rate. Together, these results highlight the potential for using chain elongating bacteria as a platform for diverse oleochemical biomanufacturing and offer a starting point for guiding future metabolic engineering efforts aimed at expanding their product range.
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Microbial overproduction of aromatic chemicals has gained considerable industrial interest and various metabolic engineering approaches have been employed in recent years to address the associated challenges. So far, most studies have used sugars (mostly glucose) or glycerol as the primary carbon source. In this study, we used ethylene glycol (EG) as the main carbon substrate. EG could be obtained from the degradation of plastic and cellulosic wastes. As a proof of concept, Escherichia coli was engineered to transform EG into L-tyrosine, a valuable aromatic amino acid. Under the best fermentation condition, the strain produced 2 g/L L-tyrosine from 10 g/L EG, outperforming glucose (the most common sugar feedstock) in the same experimental conditions. To prove the concept that EG can be converted into different aromatic chemicals, E. coli was further engineered with a similar approach to synthesize other valuable aromatic chemicals, L-phenylalanine and p-coumaric acid. Finally, waste polyethylene terephthalate (PET) bottles were degraded using acid hydrolysis and the resulting monomer EG was transformed into L-tyrosine using the engineered E. coli, yielding a comparable titer to that obtained using commercial EG. The strains developed in this study should be valuable to the community for producing valuable aromatics from EG.
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Escherichia coli , Etilenoglicol , Escherichia coli/genética , Escherichia coli/metabolismo , Etilenoglicol/metabolismo , Engenharia Metabólica/métodos , Glucose/metabolismo , Tirosina/genética , Tirosina/metabolismo , Carbono/metabolismo , FermentaçãoRESUMO
Many proteins bind transition metal ions as cofactors to carry out their biological functions. Despite binding affinities for divalent transition metal ions being predominantly dictated by the Irving-Williams series for wild-type proteins, in vivo metal ion binding specificity is ensured by intracellular mechanisms that regulate free metal ion concentrations. However, a growing area of biotechnology research considers the use of metal-binding proteins in vitro to purify specific metal ions from wastewater, where specificity is dictated by the protein's metal binding affinities. A goal of metalloprotein engineering is to modulate these affinities to improve a protein's specificity towards a particular metal; however, the quantitative relationship between the affinities and the equilibrium metal-bound protein fractions depends on the underlying binding mechanisms. Here we demonstrate a high-throughput intrinsic tryptophan fluorescence quenching method to validate binding models in multi-metal solutions for CcNikZ-II, a nickel-binding protein from Clostridium carboxidivorans. Using our validated models, we quantify the relationship between binding affinity and specificity in different classes of metal-binding models for CcNikZ-II. We further illustrate the potential relevance of data-informed models to predicting engineering targets for improved specificity.
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Clostridium , Metaloproteínas , Metais , Clostridium/metabolismo , Metais/metabolismo , Níquel , Zinco , Cobalto , Metaloproteínas/metabolismo , Engenharia de Proteínas , Modelos Químicos , Triptofano , FluorescênciaRESUMO
Biomining processes utilize microorganisms, such as Acidithiobacillus, to extract valuable metals by producing sulfuric acid and ferric ions that dissolve sulfidic minerals. However, excessive production of these compounds can result in metal structure corrosion and groundwater contamination. Synthetic biology offers a promising solution to improve Acidithiobacillus strains for sustainable, eco-friendly, and cost-effective biomining, but genetic engineering of these slow-growing microorganisms is challenging with current inefficient and time-consuming methods. To address this, we established a CRISPR-dCas9 system for gene knockdown in A. ferridurans JAGS, successfully downregulating the transcriptional levels of two genes involved in sulfur oxidation. More importantly, we constructed an all-in-one CRISPR-Cas9 system for fast and efficient genome editing in A. ferridurans JAGS, achieving seamless gene deletion (HdrB3), promoter substitution (Prus to Ptac), and exogenous gene insertion (GFP). Additionally, we created a HdrB-Rus double-edited strain and performed biomining experiments to extract Ni from pyrrhotite tailings. The engineered strain demonstrated a similar Ni recovery rate to wild-type A. ferridurans JAGS but with significantly lower production of iron ions and sulfuric acid in leachate. These high-efficient CRISPR systems provide a powerful tool for studying gene functions and creating useful recombinants for synthetic biology-assisted biomining applications in the future.
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Acidithiobacillus , Ferro , Oxirredução , Engenharia Genética , Metais , Acidithiobacillus/genéticaRESUMO
Chloroform (CF) and dichloromethane (DCM) contaminate groundwater sites around the world but can be cleaned up through bioremediation. Although several strains of Dehalobacter restrictus can reduce CF to DCM and multiple Peptococcaceae can ferment DCM, these processes cannot typically happen simultaneously due to CF sensitivity in the known DCM-degraders or electron donor competition. Here, we present a mixed microbial culture that can simultaneously metabolize CF and DCM and create an additional enrichment culture fed only DCM. Through genus-specific quantitative polymerase chain reaction, we find that Dehalobacter grows while either CF alone or DCM alone is converted, indicating its involvement in both metabolic steps. Additionally, the culture was maintained for over 1400 days without the addition of an exogenous electron donor, and through electron balance calculations, we show that DCM metabolism would produce sufficient reducing equivalents (likely hydrogen) for CF respiration. Together, these results suggest intraspecies electron transfer could occur to continually reduce CF in the culture. Minimizing the addition of electron donor reduces the cost of bioremediation, and "self-feeding" could prolong bioremediation activity long after donor addition ends. Overall, understanding this mechanism informs strategies for culture maintenance and scale-up and benefits contaminated sites where the culture is employed for remediation worldwide.
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Clorofórmio , Cloreto de Metileno , Clorofórmio/metabolismo , Cloreto de Metileno/metabolismo , Biodegradação Ambiental , Halogenação , Peptococcaceae/metabolismoRESUMO
Metabolism is precisely coordinated, with the goal of balancing fluxes to maintain robust growth. However, coordinating fluxes requires information about rates, which can only be inferred through concentrations. While flux-sensitive metabolites have been reported, the design principles underlying such sensing have not been clearly elucidated. Here we use kinetic modeling to show that substrate concentrations of thermodynamically constrained reactions reflect upstream flux and therefore carry information about rates. Then we use untargeted multi-omic data from Escherichia coli and Saccharomyces cerevisiae to show that the concentrations of some metabolites in central carbon metabolism reflect fluxes as a result of thermodynamic constraints. We then establish, using 37 real concentration-flux relationships across both organisms, that in vivo ΔG∘≥-4 kJ/mol is the threshold above which substrates are likely to be sensitive to upstream flux(es).
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Modelos BiológicosRESUMO
Rising concerns about climate change and sustainable energy have attracted efforts towards developing environmentally friendly alternatives to fossil fuels. Biosynthesis of n-butane, a highly desirable petro-chemical, fuel additive and diluent in the oil industry, remains a challenge. In this work, we first engineered enzymes Tes, Car and AD in the termination module to improve the selectivity of n-butane biosynthesis, and ancestral reconstruction and a synthetic RBS significantly improved the AD abundance. Next, we did ribosome binding site (RBS) calculation to identify potential metabolic bottlenecks, and then mitigated the bottleneck with RBS engineering and precursor propionyl-CoA addition. Furthermore, we employed a model-assisted strain design and a nonrepetitive extra-long sgRNA arrays (ELSAs) and quorum sensing assisted CRISPRi to facilitate a dynamic two-stage fermentation. Through systems engineering, n-butane production was increased by 168-fold from 0.04 to 6.74 mg/L. Finally, the maximum n-butane production from acetate was predicted using parsimonious flux balance analysis (pFBA), and we achieved n-butane production from acetate produced by electrocatalytic CO reduction. Our findings pave the way for selectively producing n-butane from renewable carbon source.
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Escherichia coli , Engenharia Metabólica , Escherichia coli/genética , Escherichia coli/metabolismo , Butanos/metabolismo , Acetatos/metabolismoRESUMO
Ethanol is one of the most widely used recreational substances in the world and due to its ubiquitous use, ethanol abuse has been the cause of over 3.3 million deaths each year. In addition to its effects, ethanol's primary metabolite, acetaldehyde, is a carcinogen that can cause symptoms of facial flushing, headaches, and nausea. How strongly ethanol or acetaldehyde affects an individual depends highly on the genetic polymorphisms of certain genes. In particular, the genetic polymorphisms of mitochondrial aldehyde dehydrogenase, ALDH2, play a large role in the metabolism of acetaldehyde. Thus, it is important to characterize how genetic variations can lead to different exposures and responses to ethanol and acetaldehyde. While the pharmacokinetics of ethanol metabolism through alcohol dehydrogenase have been thoroughly explored in previous studies, in this paper, we combined a base physiologically-based pharmacokinetic (PBPK) model with a whole-body genome-scale model (WBM) to gain further insight into the effect of other less explored processes and genetic variations on ethanol metabolism. This combined model was fit to clinical data and used to show the effect of alcohol concentrations, organ damage, ALDH2 enzyme polymorphisms, and ALDH2-inhibiting drug disulfiram on ethanol and acetaldehyde exposure. Through estimating the reaction rates of auxiliary processes with dynamic Flux Balance Analysis, The PBPK-WBM was able to navigate around a lack of kinetic constants traditionally associated with PK modelling and demonstrate the compensatory effects of the body in response to decreased liver enzyme expression. Additionally, the model demonstrated that acetaldehyde exposure increased with higher dosages of disulfiram and decreased ALDH2 efficiency, and that moderate consumption rates of ethanol could lead to unexpected accumulations in acetaldehyde. This modelling framework combines the comprehensive steady-state analyses from genome-scale models with the dynamics of traditional PK models to create a highly personalized form of PBPK modelling that can push the boundaries of precision medicine.
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Acetaldeído/metabolismo , Alcoolismo/genética , Alcoolismo/metabolismo , Etanol/metabolismo , Modelos Biológicos , Acetaldeído/farmacocinética , Acetaldeído/toxicidade , Inibidores de Acetaldeído Desidrogenases/farmacologia , Dissuasores de Álcool/farmacologia , Alcoolismo/tratamento farmacológico , Aldeído-Desidrogenase Mitocondrial/genética , Aldeído-Desidrogenase Mitocondrial/metabolismo , Biologia Computacional , Simulação por Computador , Dissulfiram/farmacologia , Etanol/farmacocinética , Etanol/toxicidade , Humanos , Absorção Intestinal/fisiologia , Cinética , Fígado/efeitos dos fármacos , Fígado/metabolismo , Masculino , Distribuição TecidualRESUMO
The study of microbial communities and their interactions has attracted the interest of the scientific community, because of their potential for applications in biotechnology, ecology and medicine. The complexity of interspecies interactions, which are key for the macroscopic behavior of microbial communities, cannot be studied easily experimentally. For this reason, the modeling of microbial communities has begun to leverage the knowledge of established constraint-based methods, which have long been used for studying and analyzing the microbial metabolism of individual species based on genome-scale metabolic reconstructions of microorganisms. A main problem of genome-scale metabolic reconstructions is that they usually contain metabolic gaps due to genome misannotations and unknown enzyme functions. This problem is traditionally solved by using gap-filling algorithms that add biochemical reactions from external databases to the metabolic reconstruction, in order to restore model growth. However, gap-filling algorithms could evolve by taking into account metabolic interactions among species that coexist in microbial communities. In this work, a gap-filling method that resolves metabolic gaps at the community level was developed. The efficacy of the algorithm was tested by analyzing its ability to resolve metabolic gaps on a synthetic community of auxotrophic Escherichia coli strains. Subsequently, the algorithm was applied to resolve metabolic gaps and predict metabolic interactions in a community of Bifidobacterium adolescentis and Faecalibacterium prausnitzii, two species present in the human gut microbiota, and in an experimentally studied community of Dehalobacter and Bacteroidales species of the ACT-3 community. The community gap-filling method can facilitate the improvement of metabolic models and the identification of metabolic interactions that are difficult to identify experimentally in microbial communities.
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Algoritmos , Redes e Vias Metabólicas , Microbiota/fisiologia , Modelos Biológicos , Bacteroidetes/metabolismo , Bifidobacterium adolescentis/metabolismo , Biologia Computacional , Simulação por Computador , Bases de Dados Factuais , Escherichia coli/metabolismo , Faecalibacterium prausnitzii/metabolismo , Microbioma Gastrointestinal/fisiologia , Humanos , Peptococcaceae/metabolismo , Biologia SintéticaRESUMO
Carbon-carbon bond formation is one of the most important reactions in biocatalysis and organic chemistry. In nature, aldolases catalyze the reversible stereoselective aldol addition between two carbonyl compounds, making them attractive catalysts for the synthesis of various chemicals. In this work, we identified several 2-deoxyribose-5-phosphate aldolases (DERAs) having acetaldehyde condensation activity, which can be used for the biosynthesis of (R)-1,3-butanediol (1,3BDO) in combination with aldo-keto reductases (AKRs). Enzymatic screening of 20 purified DERAs revealed the presence of significant acetaldehyde condensation activity in 12 of the enzymes, with the highest activities in BH1352 from Bacillus halodurans, TM1559 from Thermotoga maritima, and DeoC from Escherichia coli The crystal structures of BH1352 and TM1559 at 1.40-2.50 Å resolution are the first full-length DERA structures revealing the presence of the C-terminal Tyr (Tyr224 in BH1352). The results from structure-based site-directed mutagenesis of BH1352 indicated a key role for the catalytic Lys155 and other active-site residues in the 2-deoxyribose-5-phosphate cleavage and acetaldehyde condensation reactions. These experiments also revealed a 2.5-fold increase in acetaldehyde transformation to 1,3BDO (in combination with AKR) in the BH1352 F160Y and F160Y/M173I variants. The replacement of the WT BH1352 by the F160Y or F160Y/M173I variants in E. coli cells expressing the DERA + AKR pathway increased the production of 1,3BDO from glucose five and six times, respectively. Thus, our work provides detailed insights into the molecular mechanisms of substrate selectivity and activity of DERAs and identifies two DERA variants with enhanced activity for in vitro and in vivo 1,3BDO biosynthesis.
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Aldeído Liases/metabolismo , Bacillus/enzimologia , Butileno Glicóis/metabolismo , Escherichia coli/enzimologia , Thermotoga maritima/enzimologia , Aldeído Liases/química , Aldeído Liases/genética , Bacillus/genética , Bacillus/metabolismo , Vias Biossintéticas , Domínio Catalítico , Cristalografia por Raios X , Escherichia coli/genética , Escherichia coli/metabolismo , Microbiologia Industrial , Modelos Moleculares , Mutagênese Sítio-Dirigida , Filogenia , Engenharia de Proteínas , Thermotoga maritima/genética , Thermotoga maritima/metabolismoRESUMO
BACKGROUND: A considerable challenge in the development of bioprocesses for producing chemicals and fuels has been the high cost of feedstocks relative to oil prices, making it difficult for these processes to compete with their conventional petrochemical counterparts. Hence, in the absence of high oil prices in the near future, there has been a shift in the industry to produce higher value compounds such as fragrances for cosmetics. Yet, there is still a need to address climate change and develop biotechnological approaches for producing large market, lower value chemicals and fuels. RESULTS: In this work, we study ethylene glycol (EG), a novel feedstock that we believe has promise to address this challenge. We engineer Escherichia coli (E. coli) to consume EG and examine glycolate production as a case study for chemical production. Using a combination of modeling and experimental studies, we identify oxygen concentration as an important metabolic valve in the assimilation and use of EG as a substrate. Two oxygen-based strategies are thus developed and tested in fed-batch bioreactors. Ultimately, the best glycolate production strategy employed a target respiratory quotient leading to the highest observed fermentation performance. With this strategy, a glycolate titer of 10.4 g/L was reached after 112 h of production time in a fed-batch bioreactor. Correspondingly, a yield of 0.8 g/g from EG and productivity of 0.1 g/L h were measured during the production stage. Our modeling and experimental results clearly suggest that oxygen concentration is an important factor in the assimilation and use of EG as a substrate. Finally, our use of metabolic modeling also sheds light on the intracellular distribution through central metabolism, implicating flux to 2-phosphoglycerate as the primary route for EG assimilation. CONCLUSION: Overall, our work suggests that EG could provide a renewable starting material for commercial biosynthesis of fuels and chemicals that may achieve economic parity with petrochemical feedstocks while sequestering carbon dioxide.
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Reatores Biológicos/microbiologia , Escherichia coli/metabolismo , Etilenoglicol/metabolismo , Fermentação , Glicolatos/metabolismo , Engenharia Metabólica/métodos , Escherichia coli/genética , Formiatos/metabolismo , Glucose/metabolismo , Ácidos Glicéricos/metabolismo , Redes e Vias Metabólicas/genética , Oxigênio/metabolismo , Xilose/metabolismoRESUMO
BACKGROUND: Microorganisms can be metabolically engineered to produce a wide range of commercially important chemicals. Advancements in computational strategies for strain design and synthetic biological techniques to construct the designed strains have facilitated the generation of large libraries of potential candidates for chemical production. Consequently, there is a need for high-throughput laboratory scale techniques to characterize and screen these candidates to select strains for further investigation in large scale fermentation processes. Several small-scale fermentation techniques, in conjunction with laboratory automation have enhanced the throughput of enzyme and strain phenotyping experiments. However, such high throughput experimentation typically entails large operational costs and generate massive amounts of laboratory plastic waste. RESULTS: In this work, we develop an eco-friendly automation workflow that effectively calibrates and decontaminates fixed-tip liquid handling systems to reduce tip waste. We also investigate inexpensive methods to establish anaerobic conditions in microplates for high-throughput anaerobic phenotyping. To validate our phenotyping platform, we perform two case studies-an anaerobic enzyme screen, and a microbial phenotypic screen. We used our automation platform to investigate conditions under which several strains of E. coli exhibit the same phenotypes in 0.5 L bioreactors and in our scaled-down fermentation platform. We also propose the use of dimensionality reduction through t-distributed stochastic neighbours embedding (t-SNE) in conjunction with our phenotyping platform to effectively cluster similarly performing strains at the bioreactor scale. CONCLUSIONS: Fixed-tip liquid handling systems can significantly reduce the amount of plastic waste generated in biological laboratories and our decontamination and calibration protocols could facilitate the widespread adoption of such systems. Further, the use of t-SNE in conjunction with our automation platform could serve as an effective scale-down model for bioreactor fermentations. Finally, by integrating an in-house data-analysis pipeline, we were able to accelerate the 'test' phase of the design-build-test-learn cycle of metabolic engineering.
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Automação Laboratorial/métodos , Escherichia coli/metabolismo , Fermentação , Engenharia Metabólica/instrumentação , Engenharia Metabólica/métodos , Anaerobiose , Escherichia coli/genética , Ensaios de Triagem em Larga Escala/instrumentação , Ensaios de Triagem em Larga Escala/métodosRESUMO
The last two decades have seen vigorous activity in synthetic biology research and the ever-increasing applications of these technologies. However, pedagogical research pertaining to teaching synthetic biology is scarce, especially when compared to other science and engineering disciplines. Within Canada, there are only three universities that offer synthetic biology programs, two of which are at the undergraduate level. Rather than taking place in formal academic settings, many Canadian undergraduate students are introduced to synthetic biology through participation in the annual International Genetically Engineered Machine (iGEM) competition. Although the iGEM competition has had a transformative impact on synthetic biology training in other nations, its impact in Canada has been relatively modest. Consequently, the iGEM competition remains a major setting for synthetic biology education in Canada. To promote further development of synthetic biology education, we surveyed undergraduate students from the Canadian iGEM design teams of 2019. We extracted insights from these data using qualitative analysis to provide recommendations for best teaching practices in synthetic biology undergraduate education, which we describe through our proposed Framework for Transdisciplinary Synthetic Biology Education (FTSBE).
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Engenharia Genética , Biologia Sintética , Canadá , Humanos , Estudantes , UniversidadesRESUMO
BACKGROUND: The concept of minimal cut sets (MCS) has become an important mathematical framework for analyzing and (re)designing metabolic networks. However, the calculation of MCS in genome-scale metabolic models is a complex computational problem. The development of duality-based algorithms in the last years allowed the enumeration of thousands of MCS in genome-scale networks by solving mixed-integer linear problems (MILP). A recent advancement in this field was the introduction of the MCS2 approach. In contrast to the Farkas-lemma-based dual system used in earlier studies, the MCS2 approach employs a more condensed representation of the dual system based on the nullspace of the stoichiometric matrix, which, due to its reduced dimension, holds promise to further enhance MCS computations. RESULTS: In this work, we introduce several new variants and modifications of duality-based MCS algorithms and benchmark their effects on the overall performance. As one major result, we generalize the original MCS2 approach (which was limited to blocking the operation of certain target reactions) to the most general case of MCS computations with arbitrary target and desired regions. Building upon these developments, we introduce a new MILP variant which allows maximal flexibility in the formulation of MCS problems and fully leverages the reduced size of the nullspace-based dual system. With a comprehensive set of benchmarks, we show that the MILP with the nullspace-based dual system outperforms the MILP with the Farkas-lemma-based dual system speeding up MCS computation with an averaged factor of approximately 2.5. We furthermore present several simplifications in the formulation of constraints, mainly related to binary variables, which further enhance the performance of MCS-related MILP. However, the benchmarks also reveal that some highly condensed formulations of constraints, especially on reversible reactions, may lead to worse behavior when compared to variants with a larger number of (more explicit) constraints and involved variables. CONCLUSIONS: Our results further enhance the algorithmic toolbox for MCS calculations and are of general importance for theoretical developments as well as for practical applications of the MCS framework.
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Algoritmos , Redes e Vias Metabólicas/genética , Corynebacterium/genética , Escherichia coli/genética , Genoma , Engenharia Metabólica , Modelos Biológicos , Saccharomyces cerevisiae/genéticaRESUMO
MOTIVATION: In kinetic models of metabolism, the parameter values determine the dynamic behaviour predicted by these models. Estimating parameters from in vivo experimental data require the parameters to be structurally identifiable, and the data to be informative enough to estimate these parameters. Existing methods to determine the structural identifiability of parameters in kinetic models of metabolism can only be applied to models of small metabolic networks due to their computational complexity. Additionally, a priori experimental design, a necessity to obtain informative data for parameter estimation, also does not account for using steady-state data to estimate parameters in kinetic models. RESULTS: Here, we present a scalable methodology to structurally identify parameters for each flux in a kinetic model of metabolism based on the availability of steady-state data. In doing so, we also address the issue of determining the number and nature of experiments for generating steady-state data to estimate these parameters. By using a small metabolic network as an example, we show that most parameters in fluxes expressed by mechanistic enzyme kinetic rate laws can be identified using steady-state data, and the steady-state data required for their estimation can be obtained from selective experiments involving both substrate and enzyme level perturbations. The methodology can be used in combination with other identifiability and experimental design algorithms that use dynamic data to determine the most informative experiments requiring the least resources to perform. AVAILABILITY AND IMPLEMENTATION: https://github.com/LMSE/ident. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Modelos Biológicos , Projetos de Pesquisa , Algoritmos , Cinética , Redes e Vias MetabólicasRESUMO
Microbial metabolism can be harnessed to produce a broad range of industrially important chemicals. Often, three key process variables: Titer, Rate and Yield (TRY) are the target of metabolic engineering efforts to improve microbial hosts toward industrial production. Previous research into improving the TRY metrics have examined the efficacy of having distinct growth and production stages to achieve enhanced productivity. However, these studies assumed a switch from a maximum growth to a maximum production phenotype. Hence, phenotypes with intermediate growth and chemical production in each of the growth and production stages of two-stage processes are yet to be explored. The impact of reduced growth rates on substrate uptake adds to the need for intelligent choice of operating points while designing two-stage processes. In this work, we develop a computational framework that scans the phenotypic space of microbial metabolism to identify ideal growth and production phenotypic targets, to achieve optimal TRY targets. Using this framework, with Escherichia coli as a model organism, we compare two-stage processes that use dynamic pathway regulation, with one-stage processes that use static intervention strategies, for different bioprocess objectives. Our results indicate that two-stage processes with intermediate growth during the production stage always result in optimal TRY values even in cases where substrate uptake is limited due to reduced growth during chemical production. By analyzing the flux distributions for the production enhancing strategies, we identify key reactions and reaction subsystems that require perturbation to achieve a production phenotype for a wide range of metabolites in E. coli. Interestingly, flux perturbations that increase phosphoenolpyruvate and NADPH availability are enriched among these production phenotypes. Furthermore, reactions in the pentose phosphate pathway emerge as key control nodes that function together to increase the availability of precursors to most products in E. coli. The inherently modular nature of microbial metabolism results in common reactions and reaction subsystems that need to be regulated to modify microbes from their target of growth to the production of a diverse range of metabolites. Due to the presence of these common patterns in the flux perturbations, we propose the possibility of a universal production strain.
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Escherichia coli , Engenharia Metabólica , Escherichia coli/genética , Escherichia coli/metabolismo , NADP/metabolismo , Via de Pentose FosfatoRESUMO
Solute-binding proteins (SBPs) from ATP-binding cassette (ABC) transporters play crucial roles across all forms of life in transporting compounds against chemical gradients. Some SBPs have evolved to scavenge metal substrates from the environment with nanomolar and micromolar affinities (KD). There exist well established techniques like isothermal titration calorimetry for thoroughly studying these metalloprotein interactions with metal ions, but they are low-throughput. For protein libraries comprised of many metalloprotein homologues and mutants, and for collections of buffer conditions and potential ligands, the throughput of these techniques is paramount. In this study, we describe an improved method termed the microITFQ-LTA and validated it using CjNikZ, a well-characterized nickel-specific SBP (Ni-BP) from Campylobacter jejuni. We then demonstrated how the microITFQ-LTA can be designed to screen through a small collection of buffers and ligands to elucidate the binding profile of a putative Ni-BP from Clostridium carboxidivorans that we call CcSBPII. Through this study, we showed CcSBPII can bind to various metal ions with KD ranged over 3 orders of magnitude. In the presence of l-histidine, CcSBPII could bind to Ni2+ over 2000-fold more tightly, which was 11.6-fold tighter than CjNikZ given the same ligand.
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Proteínas de Bactérias/metabolismo , Metaloproteínas/metabolismo , Níquel/metabolismo , Proteínas de Bactérias/química , Proteínas de Bactérias/genética , Clostridium/metabolismo , Concentração de Íons de Hidrogênio , Cinética , Ligantes , Metaloproteínas/química , Metaloproteínas/genética , Análise em Microsséries/métodos , Níquel/química , Ligação Proteica , Proteínas Recombinantes/biossíntese , Proteínas Recombinantes/química , Proteínas Recombinantes/isolamento & purificação , Espectrometria de FluorescênciaRESUMO
Motivation: Metabolism can exhibit dynamic phenomena like bistability due to the presence of regulatory motifs like the positive feedback loop. As cell factories, microorganisms with bistable metabolism can have a high and a low product flux at the two stable steady states, respectively. The exclusion of metabolic regulation and network dynamics limits the ability of pseudo-steady state stoichiometric models to detect the presence of bistability, and reliably assess the outcomes of design perturbations to metabolic networks. Results: Using kinetic models of metabolism, we assess the change in the bistable characteristics of the network, and suggest designs based on perturbations to the positive feedback loop to enable the network to produce at its theoretical maximum rate. We show that the most optimal production design in parameter space, for a small bistable metabolic network, may exist at the boundary of the bistable region separating it from the monostable region of low product fluxes. The results of our analysis can be broadly applied to other bistable metabolic networks with similar positive feedback network topologies. This can complement existing model-based design strategies by providing a smaller number of feasible designs that need to be tested in vivo. Availability and implementation: http://lmse.biozone.utoronto.ca/downloads/. Contact: krishna.mahadevan@utoronto.ca. Supplementary information: Supplementary data are available at Bioinformatics online.