1,720,991 research outputs found
Computational and experimental methods for metabolic engineering: applications in Escherichia coli and Bacillus subtilis
No full textMetabolic engineering was defined more than 25 years ago as the directed modulation of metabolic pathways, using methods of recombinant DNA technology, for the purpose of overproducing high-value compounds, such as pharmaceutical products, food additives and fuels. Given the increasing need of more sustainable processes for the production of value-added chemicals and materials from renewable resources, metabolic engineering became a powerful tool for the development of highly efficient microbial cell factories.
The main innovation introduced from metabolic engineering, compared to traditional trial-and-error approaches, is the use of predictive modelling methods to study the behaviour of cellular metabolism and to guide the rational strain design. In this context, the cellular metabolism is described by the complete set of biochemical reactions that occur in the target microorganism, known as genome-scale metabolic model, and can be analyzed in terms of flux distributions, namely the reaction rates. Differently from gene expression levels or protein and metabolite concentration, the metabolic flux profiles are able to reflect the consequences of cellular component interactions.
Despite a variety of in-silico modelling approaches have been developed for the study of cellular metabolism, only those requiring a limited number of readily available parameters can be successfully applied to genome-scale models. Currently, constraint-based modelling approach is the best methodology by which genome-scale models are constructed and analyzed. This approach identifies a set of allowable solutions, by the assumption of steady-state conditions and limiting the fluxes, and then finds an unique flux distribution, by an optimization problem that maximizes or minimizes a biological objective function.
Several methods based on different objective functions, and therefore appropriate for specific study goals, were developed. Flux Balance Analysis is the most popular method, which determines the flux through the metabolic network that maximizes growth rate. However, in some contexts the reliability of such models in the quantitative prediction of cellular phenotypes and fluxes through biochemical reactions can be low. The integration of additional biological information in the model, e.g., genome-scale transcriptomic or proteomic profiles, has been recently proposed as an attempt to improve prediction accuracy.
The last and crucial step for strain improvement is the application of genetic manipulations for the control of metabolic fluxes through recombinant DNA technologies. The perturbations, identified by the in-silico design phase, are implemented through the synthetic biology techniques for the tight control of gene expression levels, namely over-, down-expression and deletion. Synthetic biology is an emerging discipline, closely coupled with metabolic engineering field, that promotes the optimization of microorganisms using toolkits of pre-characterized regulatory elements. In particular, regulatory parts, such as promoters or ribosome binding sites, are commonly used for the over- or down-regulation of transcriptional and translational processes of target genes, respectively, whereas gene knockouts are implemented using homologous recombination or silencing the gene via the new proposed techniques.
This thesis work includes both in-silico and in-vivo investigations on different metabolic engineering tools on Escherichia coli and Bacillus subtilis
Metabolic Engineering of Bacillus subtilis for the Production of Poly-γ-Glutamic Acid from Glycerol Feedstock
No full textPoly-γ-glutamic acid (γ-PGA) is an attractive biopolymer for medical, agri-food, and environmental applications. Although microbial synthesis by Bacilli fed on waste streams has been widely adopted, the obtainment of efficient sustainable production processes is still under investigation by bioprocess and metabolic engineering approaches. The abundant glycerol-rich waste generated in the biodiesel industry can be used as a carbon source for γ-PGA production. Here, we studied fermentation performance in different engineered Bacillus subtilis strains in glycerol-based media, considering a swrA+ degU32Hy mutant as the initial producer strain and glucose-based media for comparison. Modifications included engineering the biosynthetic pgs operon regulation (replacing its native promoter with Physpank), precursor accumulation (sucCD or odhAB deletion), and enhanced glutamate racemization (racE overexpression), predicted as crucial reactions by genome scale model simulations. All interventions increased productivity in glucose-based media, with Physpank-pgs ΔsucCD showing the highest γ-PGA titer (52 g/L). Weaker effects were observed in glycerol-based media: ΔsucCD and Physpank-pgs led to slight improvements under low- and highglutamate conditions, respectively, reaching ~22 g/L γ-PGA (26% increase). No performance decrease was detected by replacing pure glycerol with crude glycerol waste from a biodiesel plant, and by a 30-fold scale-up. These results may be relevant for improving industrial γ-PGA production efficiency and process sustainability using waste feedstock. The performance differences observed between glucose and glycerol media also motivate additional computational and experimental studies to design metabolically optimized strains
Synthetic and systems biology for cost-competitive γ-PGA production
No full textγ-PGA is a natural polymer secreted by Bacillus subtilis and other bacteria with huge potential in various biotechnological fields. Yet its commercial development is lagging behind due to the still high price. To increase the industrial attractiveness of this product it is therefore necessary to focus research efforts on reducing production costs. This objective can be achieved through different approaches which can be synergistically combined:
1. The expression of the pgs operon for biopolymer synthesis can be improved;
2. The bacterial metabolism can be modified
a. so that cost-competitive substrates, such as industrial or crop waste products, can be used as feedstock;
b. to accumulate the metabolic precursors required for product synthesis.
Our aim is to act at all these levels with a common strategy, i.e. exploiting tools and data made available by synthetic & systems biology.
For the first approach we are quantitatively evaluating the endogenous expression of the pgs operon, in order to tune it using pre-characterized regulatory "parts" from recently described libraries. For the second approach glycerol by-produced in biodiesel plants was chosen as low cost feedstock. We assessed the propensity of the γ-PGA producer and other B. subtilis strains to grow on glycerol analyzing its consumption flux compared to that of glucose, used until now. For optimization, the glycerol uptake and catabolic genes will be up-regulated with synthetic biology tools.
Finally, a systems biology approach, exploiting genome-scale metabolic models, was chosen to identify genes whose deletion /over-expression should allow accumulation of the metabolic precursors necessary for γ-PGA production.
The first results of such a multilevel strategy will be presented and discussed
Evaluation of different constraint-based methods to predict the growth of Escherichia coli in different environmental and genetic conditions
No full textRational engineering of protein- and CRISPRi- mediated regulation devices to design predictable interconnected circuits with reduced cell load
No full textEvaluation of sequence-to-function predictive tools to support the bottom-up design of complex genetic circuits in synthetic biology
No full textPredictable Design in Biological Engineering: Debugging of Synthetic Circuits By in Vivo and in silico Approaches
No full textQuantification of the gene silencing performances of rationally-designed synthetic small RNAs
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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