Differential Bees Flux Balance Analysis with OptKnock for In Silico Microbial Strains Optimization

Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.     

Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production

Metabolism is central to cell physiology, and metabolic disturbances play a role in numerous disease states. Despite its importance, the ability to study metabolism at a global scale using genomic technologies is limited. In principle, complete genome sequences describe the range of metabolic reactions that are possible for an organism, but cannot quantitatively describe the behaviour of these reactions. We present a novel method for modeling metabolic states using whole cell measurements of gene expression. Our method, which we call E-Flux (as a combination of flux and expression), extends the technique of Flux Balance Analysis by modeling maximum flux constraints as a function of measured gene expression. In contrast to previous methods for metabolically interpreting gene expression data, E-Flux utilizes a model of the underlying metabolic network to directly predict changes in metabolic flux capacity. We applied E-Flux to Mycobacterium tuberculosis, the bacterium that causes tuberculosis (TB). Key components of mycobacterial cell walls are mycolic acids which are targets for several first-line TB drugs. We used E-Flux to predict the impact of 75 different drugs, drug combinations, and nutrient conditions on mycolic acid biosynthesis capacity in M. tuberculosis, using a public compendium of over 400 expression arrays. We tested our method using a model of mycolic acid biosynthesis as well as on a genome-scale model of M. tuberculosis metabolism. Our method correctly predicts seven of the eight known fatty acid inhibitors in this compendium and makes accurate predictions regarding the specificity of these compounds for fatty acid biosynthesis. Our method also predicts a number of additional potential modulators of TB mycolic acid biosynthesis. E-Flux thus provides a promising new approach for algorithmically predicting metabolic state from gene expression data.     

Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation

Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.     

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from ¹³C labeling experiments and genome-scale models. The data from ¹³C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by ¹³C labeling data. A comparison shows that the results of this new method are similar to those found through ¹³C Metabolic Flux Analysis (¹³C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.     

Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model

A genome-scale metabolic model of the lactic acid bacterium Lactobacillus plantarum WCFS1 was constructed based on genomic content and experimental data. The complete model includes 721 genes, 643 reactions, and 531 metabolites. Different stoichiometric modeling techniques were used for interpretation of complex fermentation data, as L. plantarum is adapted to nutrient-rich environments and only grows in media supplemented with vitamins and amino acids. (i) Based on experimental input and output fluxes, maximal ATP production was estimated and related to growth rate. (ii) Optimization of ATP production further identified amino acid catabolic pathways that were not previously associated with free-energy metabolism. (iii) Genome-scale elementary flux mode analysis identified 28 potential futile cycles. (iv) Flux variability analysis supplemented the elementary mode analysis in identifying parallel pathways, e.g. pathways with identical end products but different co-factor usage. Strongly increased flexibility in the metabolic network was observed when strict coupling between catabolic ATP production and anabolic consumption was relaxed. These results illustrate how a genome-scale metabolic model and associated constraint-based modeling techniques can be used to analyze the physiology of growth on a complex medium rather than a minimal salts medium. However, optimization of biomass formation using the Flux Balance Analysis approach, reported to successfully predict growth rate and by product formation in Escherichia coli and Saccharomyces cerevisiae, predicted too high biomass yields that were incompatible with the observed lactate production. The reason is that this approach assumes optimal efficiency of substrate to biomass conversion, and can therefore not predict the metabolically inefficient lactate formation.     

A Study on the Fundamental Mechanism and the Evolutionary Driving Forces behind Aerobic Fermentation in Yeast

Baker’s yeast Saccharomyces cerevisiae rapidly converts sugars to ethanol and carbon dioxide at both anaerobic and aerobic conditions. The later phenomenon is called Crabtree effect and has been described in two forms, long-term and short-term effect. We have previously studied under fully controlled aerobic conditions forty yeast species for their central carbon metabolism and the presence of long-term Crabtree effect. We have also studied ten steady-state yeast cultures, pulsed them with glucose, and followed the central carbon metabolism and the appearance of ethanol at dynamic conditions. In this paper we analyzed those wet laboratory data to elucidate possible mechanisms that determine the fate of glucose in different yeast species that cover approximately 250 million years of evolutionary history. We determine overflow metabolism to be the fundamental mechanism behind both long- and short-term Crabtree effect, which originated approximately 125–150 million years ago in the Saccharomyces lineage. The “invention” of overflow metabolism was the first step in the evolution of aerobic fermentation in yeast. It provides a general strategy to increase energy production rates, which we show is positively correlated to growth. The “invention” of overflow has also simultaneously enabled rapid glucose consumption in yeast, which is a trait that could have been selected for, to “starve” competitors in nature. We also show that glucose repression of respiration is confined mainly among S. cerevisiae and closely related species that diverged after the whole genome duplication event, less than 100 million years ago. Thus, glucose repression of respiration was apparently “invented” as a second step to further increase overflow and ethanol production, to inhibit growth of other microbes. The driving force behind the initial evolutionary steps was most likely competition with other microbes to faster consume and convert sugar into biomass, in niches that were semi-anaerobic.     

Enzymatic Detoxication,Conformational Selection,and the Role of Molten Globule Active Sites

The role of conformational ensembles in enzymatic reactions remains unclear. Discussion concerning “induced fit” versus “conformational selection” has, however, ignored detoxication enzymes, which exhibit catalytic promiscuity. These enzymes dominate drug metabolism and determine drug-drug interactions. The detoxication enzyme glutathione transferase A1–1 (GSTA1–1), exploits a molten globule-like active site to achieve remarkable catalytic promiscuity wherein the substrate-free conformational ensemble is broad with barrierless transitions between states. A quantitative index of catalytic promiscuity is used to compare engineered variants of GSTA1–1 and the catalytic promiscuity correlates strongly with characteristics of the thermodynamic partition function, for the substrate-free enzymes. Access to chemically disparate transition states is encoded by the substrate-free conformational ensemble. Pre-steady state catalytic data confirm an extension of the conformational selection model, wherein different substrates select different starting conformations. The kinetic liability of the conformational breadth is minimized by a smooth landscape. We propose that “local” molten globule behavior optimizes detoxication enzymes.     

Synergy between (13)C-metabolic flux analysis and flux balance analysis for understanding metabolic adaptation to anaerobiosis in E. coli

Genome-based Flux Balance Analysis (FBA) and steady-state isotopic-labeling-based Metabolic Flux Analysis (MFA) are complimentary approaches to predicting and measuring the operation and regulation of metabolic networks. Here, genome-derived models of Escherichia coli (E. coli) metabolism were used for FBA and 13C-MFA analyses of aerobic and anaerobic growths of wild-type E. coli (K-12 MG1655) cells. Validated MFA flux maps reveal that the fraction of maintenance ATP consumption in total ATP production is about 14% higher under anaerobic (51.1%) than aerobic conditions (37.2%). FBA revealed that an increased ATP utilization is consumed by ATP synthase to secrete protons from fermentation. The TCA cycle is shown to be incomplete in aerobically growing cells and submaximal growth is due to limited oxidative phosphorylation. An FBA was successful in predicting product secretion rates in aerobic culture if both glucose and oxygen uptake measurement were constrained, but the most-frequently predicted values of internal fluxes yielded from sampling the feasible space differ substantially from MFA-derived fluxes.     

Fitness costs of female choosiness are low in a socially monogamous songbird

Female mate choice is thought to be responsible for the evolution of many extravagant male ornaments and displays, but the costs of being too selective may hinder the evolution of choosiness. Selection against choosiness may be particularly strong in socially monogamous mating systems, because females may end up without a partner and forego reproduction, especially when many females prefer the same few partners (frequency-dependent selection). Here, we quantify the fitness costs of having mating preferences that are difficult to satisfy, by manipulating the availability of preferred males. We capitalize on the recent discovery that female zebra finches (Taeniopygia guttata) prefer males of familiar song dialect. We measured female fitness in captive breeding colonies in which one-third of females were given ample opportunity to choose a mate of their preferred dialect (two-thirds of all males; “relaxed competition”), while two-thirds of the females had to compete over a limited pool of mates they preferred (one-third of all males; “high competition”). As expected, social pairings were strongly assortative with regard to song dialect. In the high-competition group, 26% of the females remained unpaired, yet they still obtained relatively high fitness by using brood parasitism as an alternative reproductive tactic. Another 31% of high-competition females paired disassortatively for song dialect. These females showed increased levels of extra-pair paternity, mostly with same-dialect males as sires, suggesting that preferences were not abolished after social pairing. However, females that paired disassortatively for song dialect did not have lower reproductive success. Overall, females in the high-competition group reached equal fitness to those that experienced relaxed competition. Our study suggests that alternative reproductive tactics such as egg dumping can help overcome the frequency-dependent costs of being selective in a monogamous mating system, thereby facilitating the evolution of female choosiness.

Being highly selective in partner choice may be problematic, because widely preferred mates are rapidly claimed. However, this study of the socially monogamous zebra finch reveals that females have evolved effective ways of coping with this situation.     

Metabolic flux analysis and visualization

One of the ultimate goals of systems biology research is to obtain a comprehensive understanding of the control mechanisms of complex cellular metabolisms. Metabolic Flux Analysis (MFA) is a important method for the quantitative estimation of intracellular metabolic flows through metabolic pathways and the elucidation of cellular physiology. The primary challenge in the use of MFA is that many biological networks are underdetermined systems; it is therefore difficult to narrow down the solution space from the stoichiometric constraints alone. In this tutorial, we present an overview of Flux Balance Analysis (FBA) and (13)C-Metabolic Flux Analysis ((13)C-MFA), both of which are frequently used to solve such underdetermined systems, and we demonstrate FBA and (13)C-MFA using the genome-scale model and the central carbon metabolism model, respectively. Furthermore, because such comprehensive study of intracellular fluxes is inherently complex, we subsequently introduce various pathway mapping and visualization tools to facilitate understanding of these data in the context of the pathways. Specific visualization of MFA results using the BioCyc Omics Viewer and Pathway Projector are shown as illustrative examples.     

Glycogen metabolism of the anammox bacterium “Candidatus Brocadia sinica”

Presence of glycogen granules in anaerobic ammonium-oxidizing (anammox) bacteria has been reported so far. However, very little is known about their glycogen metabolism and the exact roles. Here, we studied the glycogen metabolism in “Ca. Brocadia sinica” growing in continuous retentostat cultures with bicarbonate as a carbon source. The effect of the culture growth phase was investigated. During the growing phase, intracellular glycogen content increased up to 32.6 mg-glucose (g-biomass dry wt)⁻¹ while the specific growth rate and ATP/ADP ratio decreased. The accumulated glycogen begun to decrease at the onset of entering the near-zero growth phase and was consumed rapidly when substrates were depleted. This clearly indicates that glycogen was synthesized and utilized as an energy storage. The proteomic analysis revealed that “Ca. B. sinica” synthesized glycogen via three known glycogen biosynthesis pathways and simultaneously degraded during the progress of active anammox, implying that glycogen is being continuously recycled. When cells were starved, a part of stored glycogen was converted to trehalose, a potential stress protectant. This suggests that glycogen serves at least as a primary carbon source of trehalose synthesis for survival. This study provides the first physiological evidence of glycogen metabolism in anammox bacteria and its significance in survival under natural substrate-limited habitat.Subject terms: Applied microbiology, Water microbiology     

Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.     

Adaptive diversification in genes that regulate resource use in Escherichia coli

While there has been much recent focus on the ecological causes of adaptive diversification, we know less about the genetic nature of the trade-offs in resource use that create and maintain stable, diversified ecotypes. Here we show how a regulatory genetic change can contribute to sympatric diversification caused by differential resource use and maintained by negative frequency-dependent selection in Escherichia coli. During adaptation to sequential use of glucose and acetate, these bacteria differentiate into two ecotypes that differ in their growth profiles. The “slow-switcher” exhibits a long lag when switching to growth on acetate after depletion of glucose, whereas the “fast-switcher” exhibits a short switching lag. We show that the short switching time in the fast-switcher is associated with a failure to down-regulate potentially costly acetate metabolism during growth on glucose. While growing on glucose, the fast-switcher expresses malate synthase A (aceB), a critical gene for acetate metabolism that fails to be properly down-regulated because of a transposon insertion in one of its regulators. Swapping the mutant regulatory allele with the ancestral allele indicated that the transposon is in part responsible for the observed differentiation between ecological types. Our results provide a rare example of a mechanistic integration of diversifying processes at the genetic, physiological, and ecological levels.     

Global gene expression in recombinant and non-recombinant yeast Saccharomyces cerevisiae in three different metabolic states

Global gene expression of two strains of Saccharomyces cerevisiae, one recombinant (P+), accumulating large amounts of an intracellular protein Superoxide Dismutase (SOD) and one non-recombinant (P−) which does not contain the recombinant plasmid, were compared in batch culture during diauxic growth when cells were growing exponentially on glucose, when they were growing exponentially on ethanol, and in the early stationary phase when glycerol was being utilized.When comparing the gene expression for P− (and P+) during growth on ethanol to that on glucose (Eth/Gluc), overexpression is related to an increase in consumption of glycerol, activation of the TCA cycle, degradation of glycogen and metabolism of ethanol. Furthermore, 97.6% of genes (80 genes) involved in the central metabolic pathway are overexpressed. This is similar to that observed by DeRisi et al. [DeRisi, J.L., Iyer, V.R. & Brown, P.O. 1997. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278:680–686.] but very different from was observed for Metabolic Flux Analysis (MFA), where the specific growth rate is lowered to ca. 40%, the fluxes in the TCA cycle are reduced to ca. 40% (to 30% in P+), glycolysis is reduced to virtually 0 and protein synthesis to ca. 50% (to 40% in P+). Clearly it is not possible to correlate in a simple or direct way, quantitative mRNA expression levels with cell function which is shown by the Metabolic Flux Analysis (MFA).When comparing the two strains in the 3 growth stages, 4 genes were found to be under or overexpressed in all cases. The products of all of these genes are expressed at the plasma membrane or cell wall of the yeast. While comparing the strains (P+/P−) when growing on glucose, ethanol and in the early stationary phase, many of the genes of the central metabolic pathways are underexpressed in P+, which is similar to the behaviour of the metabolic fluxes of both strains (MFA). Comparing the gene expression for P− (and to some extent P+) during the early stationary phase to growth on ethanol (Stat/Eth), underexpression is generalized. This shows that the switch in metabolism between ethanol and early stationary phases has an almost instantaneous effect on gene expression but a much more retarded effect on metabolic fluxes and that the “early stationary” phase represents a “late ethanol” phase from the metabolic analysis point of view since ethanol is still present and being consumed although at a much slower rate.     

Constraint-based metabolic control analysis for rational strain engineering

The advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolite concentrations, it does not consider the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework, Network Response Analysis (NRA), for rational genetic strain design. NRA is cast as a Mixed-Integer Linear Programming problem that integrates MCA, Thermodynamically-based Flux Analysis (TFA), biologically relevant constraints, as well as genome editing restrictions into a comprehensive platform for identifying metabolic engineering targets. We show that the NRA formulation and its core constraints are equivalent to the ones of Flux Balance Analysis (FBA) and TFA, which allows it to be used for a wide range of optimization criteria and with various physiological constraints. We also show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.     

“Fish-in-Net”, a Novel Method for Cell Immobilization of Zymomonas mobilis

Background

Inorganic mesoporous materials exhibit good biocompatibility and hydrothermal stability for cell immobilization. However, it is difficult to encapsulate living cells under mild conditions, and new strategies for cell immobilization are needed. We designed a “fish-in-net” approach for encapsulation of enzymes in ordered mesoporous silica under mild conditions. The main objective of this study is to demonstrate the potential of this approach in immobilization of living cells.

Methodology/Principal Findings

Zymomonas mobilis cells were encapsulated in mesoporous silica-based materials under mild conditions by using a “fish-in-net” approach. During the encapsulation process, polyethyleneglycol was used as an additive to improve the immobilization efficiency. After encapsulation, the pore size, morphology and other features were characterized by various methods, including scanning electron microscopy, nitrogen adsorption-desorption analysis, transmission electron microscopy, fourier transform infrared spectroscopy, and elemental analysis. Furthermore, the capacity of ethanol production by immobilized Zymomonas mobilis and free Zymomonas mobilis was compared.

Conclusions/Significance

In this study, Zymomonas mobilis cells were successfully encapsulated in mesoporous silica-based materials under mild conditions by the “fish-in-net” approach. Encapsulated cells could perform normal metabolism and exhibited excellent reusability. The results presented here illustrate the enormous potential of the “fish-in-net” approach for immobilization of living cells.     

Metabolism of Benzoate, Cyclohex-1-ene Carboxylate, and Cyclohexane Carboxylate by “Syntrophus aciditrophicus” Strain SB in Syntrophic Association with H2-Using Microorganisms

The metabolism of benzoate, cyclohex-1-ene carboxylate, and cyclohexane carboxylate by “Syntrophus aciditrophicus” in cocultures with hydrogen-using microorganisms was studied. Cyclohexane carboxylate, cyclohex-1-ene carboxylate, pimelate, and glutarate (or their coenzyme A [CoA] derivatives) transiently accumulated during growth with benzoate. Identification was based on comparison of retention times and mass spectra of trimethylsilyl derivatives to the retention times and mass spectra of authentic chemical standards. ¹³C nuclear magnetic resonance spectroscopy confirmed that cyclohexane carboxylate and cyclohex-1-ene carboxylate were produced from [ring-¹³C₆]benzoate. None of the metabolites mentioned above was detected in non-substrate-amended or heat-killed controls. Cyclohexane carboxylic acid accumulated to a concentration of 260 μM, accounting for about 18% of the initial benzoate added. This compound was not detected in culture extracts of Rhodopseudomonas palustris grown phototrophically or Thauera aromatica grown under nitrate-reducing conditions. Cocultures of “S. aciditrophicus” and Methanospirillum hungatei readily metabolized cyclohexane carboxylate and cyclohex-1-ene carboxylate at a rate slightly faster than the rate of benzoate metabolism. In addition to cyclohexane carboxylate, pimelate, and glutarate, 2-hydroxycyclohexane carboxylate was detected in trace amounts in cocultures grown with cyclohex-1-ene carboxylate. Cyclohex-1-ene carboxylate, pimelate, and glutarate were detected in cocultures grown with cyclohexane carboxylate at levels similar to those found in benzoate-grown cocultures. Cell extracts of “S. aciditrophicus” grown in a coculture with Desulfovibrio sp. strain G11 with benzoate or in a pure culture with crotonate contained the following enzyme activities: an ATP-dependent benzoyl-CoA ligase, cyclohex-1-ene carboxyl-CoA hydratase, and 2-hydroxycyclohexane carboxyl-CoA dehydrogenase, as well as pimelyl-CoA dehydrogenase, glutaryl-CoA dehydrogenase, and the enzymes required for conversion of crotonyl-CoA to acetate. 2-Ketocyclohexane carboxyl-CoA hydrolase activity was detected in cell extracts of “S. aciditrophicus”-Desulfovibrio sp. strain G11 benzoate-grown cocultures but not in crotonate-grown pure cultures of “S. aciditrophicus”. These results are consistent with the hypothesis that ring reduction during syntrophic benzoate metabolism involves a four- or six-electron reduction step and that once cyclohex-1-ene carboxyl-CoA is made, it is metabolized in a manner similar to that in R. palustris.     

A procedure for the estimation over time of metabolic fluxes in scenarios where measurements are uncertain and/or insufficient

Background  

An indirect approach is usually used to estimate the metabolic fluxes of an organism: couple the available measurements with known biological constraints (e.g. stoichiometry). Typically this estimation is done under a static point of view. Therefore, the fluxes so obtained are only valid while the environmental conditions and the cell state remain stable. However, estimating the evolution over time of the metabolic fluxes is valuable to investigate the dynamic behaviour of an organism and also to monitor industrial processes. Although Metabolic Flux Analysis can be successively applied with this aim, this approach has two drawbacks: i) sometimes it cannot be used because there is a lack of measurable fluxes, and ii) the uncertainty of experimental measurements cannot be considered. The Flux Balance Analysis could be used instead, but the assumption of optimal behaviour of the organism brings other difficulties.