41 research outputs found
Computational studies of industrial hosts for improved production of the 2nd generation of biofuels
No full textIn an effort of overcoming the limited availability of fossil energy resources and moving toward a sustainable economy, the focus of the research and development in the area of biofuels has shifted towards developing the 2nd generation of fuels that should be produced via microbial fermentation. The 2nd generation biofuels should satisfy several criteria such as lower emission, higher energy density and should be less corrosive to engines. Although for many of these molecules, natural producers are known, they are not produced in the appreciated quantities. Heterologous expression of biosynthetic pathways taken from natural producers or expression of de novo synthetic pathways into microbial workhorses such as E. coli allows for production of a wide spectra of biofuels. Recently, P. putida has emerged as an amenable production hosts with a number of advantages over natural producers. P. putida is a non-pathogenic soil bacterium known for its versatile metabolism. This highly adaptive bacterium has been found to survive and grow on a wide range of substrates from pure caffeine to toxic industrial waste. Moreover, P. putida is tolerant to high toxicity compounds such as 2nd generation biofuel butanol. Counterintuitively, P. putida was seldom used as a host for the production of biofuels.
In this thesis, we performed a computational analysis of this organism to evaluate its metabolic capacities to serve as a potential 2nd generation biofuels production host. Its capacity was compared against heavily used host E. coli on the test example of production of one of the most prominent fuel candidate Methyl Ethyl Ketone (MEK).
To this end, we first performed a thermodynamic curation of the genome-scale iJN1411 model of P. putida, and we then used redGEM and lumpGEM algorithms to derive a consistently reduced large-scale stoichiometric model of P. putida. We integrated different omics data into resulting models and we proposed a novel way of constraining concentrations of the same species across different compartments while maintaining the consistency with the experimental measurements. To assess its capability to serve as a host, we evaluated and analyzed more than 3.6 millions biosynthetic pathways for production of 5 MEK precursors, in both heavily used industrial workhorse E. coli and rising P. putida. We compared their capability and performance with respect to thermodynamic feasibility and yield and we identified the most promising pathways for MEK production. Beside the discovered and evaluated pathways, we present a new way of clustering of feasible pathways and pathway precursors that allows us to classify and evaluate alternative ways for production and to better understand chemistry that leads towards the target molecule.
Identification of metabolic engineering targets for the improved biofuel production requires kinetic models. We used the ORACLE framework to generate a population of large-scale kinetic models of P. putida, and we employed these models in two studies. In the first study, for a wild-type strain of P. putida grown under aerobic conditions using glucose as a carbon source, we evaluated and validated the predictions of the generated kinetic models against a collection of experimental single-gene knockouts. In the second study, we analyzed the capacity of P. putida to adapt to increased energy demand, and we identified potential metabolic engineering targets for improved resistance of this organism to stress conditions.LCS
Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies
No full textBackground: Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models. Results: In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complex-ity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand. Conclusions: The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemi-cals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.LCS
Computational analysis of Pseudomonas Putida metabolism Using Large-scale Kinetic Models
No full textP. putida is a highly adaptive, non-pathogenic, soil bacterium that can grow on a wide range of substrates, and it is tolerant to high toxicity compounds. For these reasons, it emerged recently as one of the most promising production hosts for a wide range of chemicals. In this work, we performed a computational analysis of this organism to evaluate its metabolic capacities and design metabolic engineering strategies to improve its robustness to stress conditions. To this end, we first performed a thermodynamic curation of the genome-scale iJN1411 model of P. putida KT2440, and we then used redGEM and lumpGEM algorithms to derive a consistently reduced large-scale stoichiometric model of P. putida. We integrated experimental data into the resulting core stoichiometric model, and we computed the thermodynamically-consistent steady state of metabolic fluxes. We then used the ORACLE framework to generate a population of large-scale kinetic models around the computed steady state, and we employed these models in two studies. In the first study, for wild-type strain of P. putida KT2440 grown under aerobic conditions using glucose as a carbon source, we evaluated and validated the predictions of the generated kinetic models against a collection of experimental single-gene knockouts. In the second study, we analyzed the capacity of P. putida to adapt to increased energy demand and we identified potential metabolic engineering targets for improved resistance of this organism to stress conditions. This work demonstrates the potential and usefulness of kinetic models in rational metabolic engineering strategies for (i) understanding the physiology of production hosts, (ii) optimizing production pathways, and (iii) improving the metabolic responses of organisms to environmental stresses.LCS
Predicted responses of a large-scale Pseudomonas putida KT2440 kinetic metabolic model to several single-gene knockouts are consistent with experimental observations
No full textP. putida emerged as one of the most promising production hosts for a wide range of chemicals, due to its fast growth with a low nutrient and cellular energy demand, considerable metabolic versatility, ability to grow in wide range of chemicals, suitability for genetic manipulations and its robustness and high flexibility to adapt and counteract different stresses. One of the main advantages of P. putida compared to commonly used industrial hosts such as E. coli is its superior tolerance to toxic compounds such as benzene, toluene, ethylbenzene, xylene and other hydrocarbons. In this work, we developed a large-scale kinetic model of P.putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. We first performed a gap-filling and thermodynamic curation of the genome-scale iJN1411 model of P. putida KT2440. The redGEM and lumpGEM algorithms for the systematic reduction of stoichiometric genome-scale models are then applied to the curated iJN1411 to derive a consistently reduced large-scale stoichiometric model of P. putida. Using this model as a scaffold, we next employed the ORACLE framework to generate a population of large-scale kinetic models around the experimentally observed steady state. To illustrate the predictive capabilities of these models, we performed two studies. First, for a wild-type strain of P. putida KT2440 growing on glucose under aerobic conditions, we computed metabolic responses to several single-gene knockouts, and the developed kinetic models successfully captured the experimentally observed phenotypes. In the second study, we proposed metabolic engineering interventions for improved robustness of this organism to stress conditions. Overall, the results from these studies suggest that the developed models of P. putida metabolism can successfully be used for metabolic engineering design.LCS
Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies
No full textControl Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks
No full textAnalysis of the dynamic
and steady-state properties of biochemical
networks hinges on information about the parameters of enzyme kinetics.
The lack of experimental data characterizing enzyme activities and
kinetics along with the associated uncertainties impedes the development
of kinetic models, and researchers commonly use Monte Carlo sampling
to explore the parameter space. However, the sampling of parameter
spaces is a computationally expensive task for larger biochemical
networks. To address this issue, we exploit the fact that reaction
rates of biochemical reactions and network responses can be expressed
as a function of displacements from the thermodynamic equilibrium
of elementary reaction steps and concentrations of free enzymes and
their intermediary complexes. For a set of kinetic mechanisms ubiquitously
found in biochemistry, we express kinetic responses of enzymes to
changes in network metabolite concentrations through these quantities
both analytically and schematically. The tailor-made sampling of these
quantities allows for characterizing efficiently the missing kinetic
parameters and accelerating the efforts toward building genome-scale
kinetic metabolic models, and further, it advances efforts in the
Bayesian inference context. The proposed schematic method is simple
and lends itself to a computer implementation that can be computationally
more efficient than computer implementations of similar schematic
methods
Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes
No full textThe overarching ambition of kinetic metabolic modeling is to capture the dynamic behavior of metabolism to such an extent that systems and synthetic biology strategies can reliably be tested in silico. The lack of kinetic data hampers the development of kinetic models, and most of the current models use ad hoc reduced stoichiometry or oversimplified kinetic rate expressions, which may limit their predictive strength. There is a need to introduce the community-level standards that will organize and accelerate the future developments in this area. We introduce here a set of requirements that will ensure the model quality, we examine the current kinetic models with respect to these requirements, and we propose a general workflow for constructing models that satisfy these requirements
Additional file 2 of Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies
No full textAdditional file 2: Figure S4. Distribution of the control coefficients of glucose uptake (GLCtex) with respect to most important enzymes in the stress conditions
Additional file 12 of Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies
No full textAdditional file 12: Figure S3. Thermodynamic-based variability analysis (TVA) on reactions from glycolysis, gluconeogenesis, pentose phosphate pathway and citric acid cycle of D2 (red) and GEM (black)
A Study of Dynamic Responses of E. coli Metabolism Upon Large Perturbations Using Large-Scale Kinetic Models
No full textIn recent years, constraint-based approaches have been widely used for analyzing cellular metabolism. These approaches use stoichiometric models for the characterization of the intracellular fluxes at steady state. Nonetheless, the stoichiometric models lack information about metabolic regulation and enzyme kinetics, and they are not able to capture the dynamic features of metabolic pathways. As a result, there are ongoing efforts to build large-scale and genome-scale kinetic models as they can predict the complex dynamic responses of metabolism to environmental and genetic perturbations. The development of kinetic models is mostly hindered by structural, e.g. unknown kinetic mechanisms, and quantitative, e.g. inconsistencies in the available kinetic data, uncertainties. To overcome these difficulties, we have developed the ORACLE (Optimization and Risk Analysis of Complex Living Entities) framework that uses Monte Carlo sampling techniques to build populations of kinetic models that are consistent with the observations while satisfying the stoichiometric and thermodynamic constraints. ORACLE was initially developed within the context of Metabolic Control Analysis (MCA) to compute control coefficients despite scarce information about kinetic properties of enzymes. Recently, we extended ORACLE capabilities beyond computing control coefficients to construct populations of a large class of nonlinear models of metabolism. In this work, we used the ORACLE framework to build a population of large-scale nonlinear models of aerobically grown wild-type E. coli. We used these models to investigate the dynamic responses of E. coli metabolism to multiple gene deletion knockouts and we compared them with the experiments. We further compared these predictions with the predictions obtained through MCA to assess to what extent the simpler MCA predictions are able to predict the responses of metabolism to large perturbations. These results demonstrate usefulness of large-scale kinetic models for quantitative and qualitative understanding of the global regulation of the cell.LCS
