Availability, low price, and high degree of reduction have made glycerol a highly attractive and exploited carbon source for the production of fuels and reduced chemicals. Here we report the quantitative analysis of the fermentative metabolism of glycerol in Escherichia coli through the use of kinetic modeling and metabolic control analysis (MCA) to gain a better understanding of glycerol fermentation and identify key targets for genetic manipulation that could enhance product synthesis. The kinetics of glycerol fermentation in a batch culture was simulated using a dynamic model consisting of mass balances for glycerol, ethanol, biomass, and 11 intracellular metabolites, along with the corresponding kinetic expressions for the metabolism of each species. The model was then used to calculate metabolic control coefficients and elucidate the control structure of the pathways involved in glycerol utilization and ethanol synthesis. The calculated flux control coefficients indicate that the glycolytic flux during glycerol fermentation is almost exclusively controlled by the enzymes glycerol dehydrogenase (encoded by gldA) and dihydroxyacetone kinase (DHAK) (encoded by dhaKLM). In agreement with the MCA findings, overexpression of gldA and dhaKLM led to significant increase in glycerol utilization and ethanol synthesis fluxes. Moreover, overexpression of other enzymes involved in the pathways that mediate glycerol utilization and its conversion to ethanol had no significant impact on glycerol utilization and ethanol synthesis, further validating the MCA predictions. These findings were then applied as a means of increasing the production of ethanol: overexpression of glycerol dehyrdogenase and DHAK enabled the production of 20 g/L ethanol from crude glycerol, a by-product of biodiesel production, indicating the potential for industrial scale conversion of waste glycerol to ethanol under anaerobic conditions.

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The Atacama Desert is the most arid desert on Earth, focus of important research activities related to microbial biodiversity studies. In this context, metabolic characterization of arid soil bacteria is crucial to understand their survival strategies under extreme environmental stress. We investigated whether strain-specific features of two Microbacterium species were involved in the metabolic ability to tolerate/ adapt to local variations within an extreme desert environment. Using an integrative systems biology approach we have carried out construction and comparison of genome-scale metabolic models (GEMs) of two Microbacterium sp., CGR1 and CGR2, previously isolated from physicochemically contrasting soil sites in the Atacama Desert. Despite CGR1 and CGR2 belong to different phylogenetic clades, metabolic pathways and attributes are highly conserved in both strains. However, comparison of the GEMs showed significant differences in the connectivity of specific metabolites related to pH tolerance and co 2 production. The latter is most likely required to handle acidic stress through decarboxylation reactions. We observed greater GEM connectivity within Microbacterium sp. CGR1 compared to CGR2, which is correlated with the capacity of CGR1 to tolerate a wider pH tolerance range. Both metabolic models predict the synthesis of pigment metabolites (β-carotene), observation validated by HPLC experiments. Our study provides a valuable resource to further investigate global metabolic adaptations of bacterial species to grow in soils with different abiotic factors within an extreme environment. In recent years, there has been increased interest in the study of Actinobacteria from desert areas. As noted by previous researchers, this class possess bacteria with a variety of attractive metabolic qualities that make them good candidates to survive in desert areas 1 , including their extensive metabolic capability to degrade and utilize compounds from nutrient poor environments and their ability to synthesize secondary metabolites and natural antibiotics 2. In fact, Actinobacteria is the dominant class present in desert locations 3 , and their ability to adapt to this kind of environment is the focus of intense research. For example, Lynch and collaborators found that over 98% of the lineages present in the vicinity of a volcano in the Atacama Desert corresponded to Actinobacteria, and that their metabolisms were adapted to utilize atmospheric gases present in this ecosystem 4. One genus that

Fatty acids (FAs) are essential components of cellular structure and energy-generating routes in living organisms. They exist in a variety of chemical configurations and functionalities and are catabolized by different oxidative routes, according to their structure. α- and ω-Oxidation are minor routes that occur only in eukaryotes, while β-oxidation is the major degradation route in eukaroytes and prokaryotes. These pathways have been characterized and engineered from different perspectives for industrial and biomedical applications. The severity of FA oxidation disorders in humans initially guided the study of FA metabolism at a molecular-level. On the other hand, recent advances in metabolic engineering and systems biology have powered the study of FA biosynthetic and catabolic routes in microorganisms at a systems-level. Several studies have proposed these pathways as platforms for the production of fuels and chemicals from biorenewable sources. The lower complexity of microbial systems has allowed a more comprehensive study of FA metabolism and has opened opportunities for a wider range of applications. Still, there is a need for techniques that facilitate the translation of high-throughput data from microorganisms to more complex eukaryotic systems in order to aid the development of diagnostic and treatment strategies for FA oxidation disorders. In addition, further systems biology analyses on human systems could also provide valuable insights on oxidation disorders. This article presents a comparison of the three main FA oxidative routes, systems biology analyses that have been used to study FA metabolism, and engineering efforts performed on microbial systems.

A continuous model of a metabolic network including gene regulation to simulate metabolic fluxes during batch cultivation of yeast Saccharomyces cerevisiae was developed. The metabolic network includes reactions of glycolysis, gluconeogenesis, glycerol and ethanol synthesis and consumption, the tricarboxylic acid cycle, and protein synthesis. Carbon sources considered were glucose and then ethanol synthesized during growth on glucose. The metabolic network has 39 fluxes, which represent the action of 50 enzymes and 64 genes and it is coupled with a gene regulation network which defines enzyme synthesis (activities) and incorporates regulation by glucose (enzyme induction and repression), modeled using ordinary differential equations. The model includes enzyme kinetics, equations that follow both mass‐action law and transport as well as inducible, repressible, and constitutive enzymes of metabolism. The model was able to simulate a fermentation of S. cerevisiae during the exponential growth phase on glucose and the exponential growth phase on ethanol using only one set of kinetic parameters. All fluxes in the continuous model followed the behavior shown by the metabolic flux analysis (MFA) obtained from experimental results. The differences obtained between the fluxes given by the model and the fluxes determined by the MFA do not exceed 25% in 75% of the cases during exponential growth on glucose, and 20% in 90% of the cases during exponential growth on ethanol. Furthermore, the adjustment of the fermentation profiles of biomass, glucose, and ethanol were 95%, 95%, and 79%, respectively. With these results the simulation was considered successful. A comparison between the simulation of the continuous model and the experimental data of the diauxic yeast fermentation for glucose, biomass, and ethanol, shows an extremely good match using the parameters found. The small discrepancies between the fluxes obtained through MFA and those predicted by the differential equations, as well as the good match between the profiles of glucose, biomass, and ethanol, and our simulation, show that this simple model, that does not rely on complex kinetic expressions, is able to capture the global behavior of the experimental data. Also, the determination of parameters using a straightforward minimization technique using data at only two points in time was sufficient to produce a relatively accurate model. Thus, even with a small amount of experimental data (rates and not concentrations) it was possible to estimate the parameters minimizing a simple objective function. The method proposed allows the obtention of reasonable parameters and concentrations in a system with a much larger number of unknowns than equations. Hence a contribution of this study is to present a convenient way to find in vivo rate parameters to model metabolic and genetic networks under different conditions. Biotechnol. Bioeng. 2012;109: 2325–2339. © 2012 Wiley Periodicals, Inc.

Most microorganisms can metabolize glycerol when external electron acceptors are available (i.e. under respiratory conditions). However, few can do so under fermentative conditions owing to the unique redox constraints imposed by the high degree of reduction of glycerol. Here, we utilize in silico analysis combined with in vivo genetic and biochemical approaches to investigate the fermentative metabolism of glycerol in Escherichia coli. We found that E. coli can achieve redox balance at alkaline pH by reducing protons to H 2 , complementing the previously reported role of 1,2-propanediol synthesis under acidic conditions. In this new redox balancing mode, H 2 evolution is coupled to a respiratory glycerol dissimilation pathway composed of glycerol kinase (GK) and glycerol-3-phosphate (G3P) dehydrogenase (G3PDH). GK activates glycerol to G3P, which is further oxidized by G3PDH to generate reduced quinones that drive hydrogenase-dependent H 2 evolution. Despite the importance of the GK-G3PDH route under alkaline conditions, we found that the NADH-generating glycerol dissimilation pathway via glycerol dehydrogenase (GldA) and phosphoenolpyruvate (PEP)-dependent dihydroxyacetone kinase (DHAK) was essential under both alkaline and acidic conditions. We assessed system-wide metabolic impacts of the constraints imposed by the PEP dependency of the GldA-DHAK route. This included the identification of enzymes and pathways that were not previously known to be involved in glycerol metabolisms such as PEP carboxykinase, PEP synthetase, multiple fructose-1,6-bisphosphatases and the fructose phosphate bypass.

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