Thermodynamic analysis of regulation in metabolic networks using constraint-based modeling

<p>Abstract</p> <p>Background</p> <p><it>Geobacter sulfurreducens </it>is a member of the <it>Geobacter </it>species, which are capable of oxidation of organic waste coupled to the reduction of heavy metals and electrode with applications in bioremediation and bioenergy generation. While the metabolism of this organism has been studied through the development of a stoichiometry based genome-scale metabolic model, the associated regulatory network has not yet been well studied. In this manuscript, we report on the implementation of a thermodynamics based metabolic flux model for <it>Geobacter sulfurreducens</it>. We use this updated model to identify reactions that are subject to regulatory control in the metabolic network of <it>G. sulfurreducens </it>using thermodynamic variability analysis.</p> <p>Findings</p> <p>As a first step, we have validated the regulatory sites and bottleneck reactions predicted by the thermodynamic flux analysis in <it>E. coli </it>by evaluating the expression ranges of the corresponding genes. We then identified ten reactions in the metabolic network of <it>G. sulfurreducens </it>that are predicted to be candidates for regulation. We then compared the free energy ranges for these reactions with the corresponding gene expression fold changes under conditions of different environmental and genetic perturbations and show that the model predictions of regulation are consistent with data. In addition, we also identify reactions that operate close to equilibrium and show that the experimentally determined exchange coefficient (a measure of reversibility) is significant for these reactions.</p> <p>Conclusions</p> <p>Application of the thermodynamic constraints resulted in identification of potential bottleneck reactions not only from the central metabolism but also from the nucleotide and amino acid subsystems, thereby showing the highly coupled nature of the thermodynamic constraints. In addition, thermodynamic variability analysis serves as a valuable tool in estimating the ranges of Δ_rG' of every reaction in the model leading to the prediction of regulatory sites in the metabolic network, thereby characterizing the regulatory network in both a model organism such as <it>E. coli </it>as well as a non model organism such as <it>G. sulfurreducens</it>.</p

Model Based Prediction of Physiology of G. sulfurreducens by Flux Balance and Thermodynamics Based Metabolic Flux Analysis Approaches

The development of genome scale metabolic models have been aided by the increasing availability of genome sequences of microorganisms such as Geobacter sulfurreducens, involved in environmentally relevant processes such as the in-situ bioremediation of U(VI). Since microbial activities are the major driving forces for geochemical changes in the sub-surface, understanding of microbial behavior under a given set of conditions can help predict the likely outcome of potential subsurface bioremediation strategies. Hence, a model based lookup table was created to capture the variation in physiology of G. sulfurreducens in response to environmental perturbations. Thermodynamically feasible flux distributions were generated by incorporating thermodynamic constraints in the model. These constraints together with the mass balance constraints formed the thermodynamics based metabolic flux analysis model (TMFA). Metabolomics experiments were performed to determine the concentration of intracellular metabolites. These concentrations were posed as constraints in the TMFA model to improve the model accuracy.MAS

Abiotic and Biotic Leaching Characteristics of Pyrrhotite Tailings From the Sudbury, Ontario Area

The present study investigated the abiotic and biotic leaching characteristics of nickeliferous non-upgraded pyrrhotite (Po) tailings (0.6 wt% Ni), and upgraded Po tailings (1 wt% Ni) produced by Vale Base Metals in the Sudbury Basin of Ontario, with the aim of maximizing Ni and S0 recoveries. Mineralogical characterization of the tailings revealed the deportment of Ni to be similar in the non-upgraded and upgraded tailings, with 60% of the total Ni locked in Po, and the balance 40% associated with pentlandite (Pn). Application of a bacterial adaptive evolution protocol on 1%-20% (w/v) non-upgraded Po tailings, showed that the extent of Ni dissolution is positively correlated with microbial activity. Subsequently, a selective-dissolution protocol was developed to relate the overall Ni dissolution with the individual terminal dissolution extent of Po and Pn as functions of four leaching regimes: ‘anoxic acid’ (with and without pH control), ‘oxic acid’ (O2 sparging), ‘oxic acid’ (air sparging), and ‘oxic ferric’ (air sparging). The results showed that the maximum Ni dissolution (94%) was obtained during the pH controlled oxic acid leach with O2 sparging at pH 1.5, while the anoxic acid leach at pH 1.5 resulted in minimum Ni dissolution (10-15%) from Po. Application of the selective dissolution protocol to leach residue samples showed that Po and Pn dissolve simultaneously in the presence of Fe(III) and O2, in contrast to the preferential dissolution of Po in the absence of Fe(III). The maximum yield of elemental sulfur (65%) was obtained during the oxic Fe(III) leach at pH 0.6. However, the oxic ferric leach tests did not result in increased Ni dissolution due to the formation of a protective sulfur coating around Po grains. Thus, the present study has shown that the recovery of Ni and the yield of S0 cannot be maximized simultaneously under the experimental conditions tested; thereby, leading to the identification of operating conditions that maximize the oxidative-dissolution kinetics of Po and Pn (oxic acid leach with O2 sparging at pH 1.5), and the yield of elemental sulfur (oxic ferric leach at pH 0.6).Ph.D

<span style="font-size:14.0pt;line-height: 115%;font-family:"Times New Roman";mso-fareast-font-family:"Times New Roman"; color:black;mso-ansi-language:EN-IN;mso-fareast-language:EN-IN;mso-bidi-language: HI" lang="EN-IN">Biosorption and elution of chromium from immobilized <i>Bacillus coagulans </i>biomass</span>

986-990<span style="font-size:14.0pt;line-height: 115%;font-family:" times="" new="" roman";mso-fareast-font-family:"times="" roman";="" color:black;mso-ansi-language:en-in;mso-fareast-language:en-in;mso-bidi-language:="" hi"="" lang="EN-IN">Bacillus coagu1ans, <span style="font-size:14.0pt; line-height:115%;font-family:" times="" new="" roman";mso-fareast-font-family:"times="" roman";="" color:black;mso-ansi-language:en-in;mso-fareast-language:en-in;mso-bidi-language:="" hi"="" lang="EN-IN">a tannery wastewater isolate, previously shown to bind dissolved Cr(VI), retained its ability to biosorb Cr(VI) in different matrices. Polymeric materials like agar, agarose, calcium alginate and polyacrylamide were screened. Agarose emerged as the suitable candidate for biomass immobilization mainly due to its higher stability and integrity in acidic pH. Aptness of agarose as the matrix for B. coagulans biomass was revealed during Cr(VI) biosorption from natural wastewater.</span

Direct Coupling of a Genome-Scale Microbial in Silico Model and Groundwater Reactive Transport Model

The activity of microorganisms often plays an important role in dynamic natural attenuation or engineered bioremediation of subsurface contaminants, such as chlorinated solvents, metals, and radionuclides. To evaluate and/or design bioremediated systems, quantitative reactive transport models are needed. State-of-the-art reactive transport models often ignore the microbial effects or simulate the microbial effects with static growth yield and constant reaction rate parameters over simulated conditions, while in reality microorganisms can dynamically modify their functionality (such as utilization of alternative respiratory pathways) in response to spatial and temporal variations in environmental conditions. Constraint-based genome-scale microbial in silico models, using genomic data and multiple-pathway reaction networks, have been shown to be able to simulate transient metabolism of some well studied microorganisms and identify growth rate, substrate uptake rates, and byproduct rates under different growth conditions. These rates can be identified and used to replace specific microbially-mediated reaction rates in a reactive transport model using local geochemical conditions as constraints. We previously demonstrated the potential utility of integrating a constraint-based microbial metabolism model with a reactive transport simulator as applied to bioremediation of uranium in groundwater. However, that work relied on an indirect coupling approach that was effective for initial demonstration but may not be extensible to more complex problems that are of significant interest (e.g., communities of microbial species and multiple constraining variables). Here, we extend that work by presenting and demonstrating a method of directly integrating a reactive transport model (FORTRAN code) with constraint-based in silico models solved with IBM ILOG CPLEX linear optimizer base system (C library). The models were integrated with BABEL, a language interoperability tool. The modeling system is designed in such a way that constraint-based models targeting different microorganisms or competing organism communities can be easily plugged into the system. Constraint-based modeling is very costly given the size of a genome-scale reaction network. To save computation time, a binary tree is traversed to examine the concentration and solution pool generated during the simulation in order to decide whether the constraint-based model should be called. We also show preliminary results from the integrated model including a comparison of the direct and indirect coupling approaches and evaluated the ability of the approach to simulate field experiment

Coupling a Genome-Scale Metabolic Model with a Reactive Transport Model to Describe in Situ Uranium Bioremediation

The increasing availability of the genome sequences of microorganisms involved in important bioremediation processes makes it feasible to consider developing genome-scale models that can aid in predicting the likely outcome of potential subsurface bioremediation strategies. Previous studies of the in situ bioremediation of uranium-contaminated groundwater have demonstrated that Geobacter species are often the dominant members of the groundwater community during active bioremediation and the primary organisms catalysing U(VI) reduction. Therefore, a genome-scale, constraint-based model of the metabolism of Geobacter sulfurreducens was coupled with the reactive transport model HYDROGEOCHEM in an attempt to model in situ uranium bioremediation. In order to simplify the modelling, the influence of only three growth factors was considered: acetate, the electron donor added to stimulate U(VI) reduction; Fe(III), the electron acceptor primarily supporting growth of Geobacter; and ammonium, a key nutrient. The constraint-based model predicted that growth yields of Geobacter varied significantly based on the availability of these three growth factors and that there are minimum thresholds of acetate and Fe(III) below which growth and activity are not possible. This contrasts with typical, empirical microbial models that assume fixed growth yields and the possibility for complete metabolism of the substrates. The coupled genome-scale and reactive transport model predicted acetate concentrations and U(VI) reduction rates in a field trial of in situ uranium bioremediation that were comparable to the predictions of a calibrated conventional model, but without the need for empirical calibration, other than specifying the initial biomass of Geobacter. These results suggest that coupling genome-scale metabolic models with reactive transport models may be a good approach to developing models that can be truly predictive, without empirical calibration, for evaluating the probable response of subsurface microorganisms to possible bioremediation approaches prior to implementation

Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant

This paper deals with the Markov modeling and reliability analysis of urea synthesis system of a fertilizer plant. This system was modeled using Markov birth-death process with the assumption that the failure and repair rates of each subsystem follow exponential distribution. The first-order Chapman-Kolmogorov differential equations are developed with the use of mnemonic rule and these equations are solved with Runga-Kutta fourth-order method. The long-run availability, reliability and mean time between failures are computed for various choices of failure and repair rates of subsystems of the system. The findings of the paper are discussed with the plant personnel to adopt and practice suitable maintenance policies/strategies to enhance the performance of the urea synthesis system of the fertilizer plant