Expanded thermodynamic model for microbial true yield prediction

INDISIM-Denitrification, an individual-based model for study the denitrification process

Denitrification is one of the key processes of the global nitrogen (N) cycle driven by bacteria. It has been widely known for more than 100 years as a process by which the biogeochemical N-cycle is balanced. To study this process, we develop an individual-based model called INDISIM-Denitrification. The model embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM and is designed to simulate in aerobic and anaerobic conditions the cell growth kinetics of denitrifying bacteria. INDISIM-Denitrification simulates a bioreactor that contains a culture medium with succinate as a carbon source, ammonium as nitrogen source and various electron acceptors. To implement INDISIM-Denitrification, the individual-based model INDISIM was used to give sub-models for nutrient uptake, stirring and reproduction cycle. Using a thermodynamic approach, the denitrification pathway, cellular maintenance and individual mass degradation were modeled using microbial metabolic reactions. These equations are the basis of the sub-models for metabolic maintenance, individual mass synthesis and reducing internal cytotoxic products. The model was implemented in the open-access platform NetLogo. INDISIM-Denitrification is validated using a set of experimental data of two denitrifying bacteria in two different experimental conditions. This provides an interactive tool to study the denitrification process carried out by any denitrifying bacterium since INDISIM-Denitrification allows changes in the microbial empirical formula and in the energy-transfer-efficiency used to represent the metabolic pathways involved in the denitrification process. The simulator can be obtained from the authors on request.

Fate of degraded pollutants in waste gas biofiltration: An overview of carbon end-points

The fate of the carbon from degraded pollutants in biofiltration is not well understood. The issue of missing carbon needs to be addressed quantitatively to better understand and model biofilter performance. Elucidating the various carbon end-points in various phases should contribute to the fundamental understanding of the degradation kinetics and metabolic pathways as a function of various environmental parameters. This article reviews the implications of key environmental parameters on the carbon end-points. Various studies are evaluated reporting carbon recovery over a multitude of parameters and operational conditions with respect to the analytical measurements and reported distribution of the carbon end-points.

Microbial growth yield estimates from thermodynamics and its importance for degradation of pesticides and formation of biogenic non-extractable residues

In biodegradation studies with isotope-labelled pesticides, fractions of non-extractable residues (NER) remain, but their nature and composition is rarely known, leading to uncertainty about their risk. Microbial growth leads to incorporation of carbon into the microbial mass, resulting in biogenic NER. Formation of microbial mass can be estimated from the microbial growth yield, but experimental data is rare. Instead, we suggest using prediction methods for the theoretical yield based on thermodynamics. Recently, we presented the Microbial Turnover to Biomass (MTB) method that needs a minimum of input data. We have estimated the growth yield of 40 organic chemicals (31 pesticides) using the MTB and two existing methods. The results were compared to experimental values, and the sensitivity of the methods was assessed. The MTB method performed best for pesticides. Having the theoretical yield and using the released CO₂ as a measure for microbial activity, we predicted a range for the formation of biogenic NER. For the majority of the pesticides, a considerable fraction of the NER was estimated to be biogenic. This novel approach provides a theoretical foundation applicable to the evaluation and prediction of biogenic NER formation during pesticide degradation experiments, and may also be employed for the interpretation of NER data from regulatory studies.

MbT-Tool: An open-access tool based on Thermodynamic Electron Equivalents Model to obtain microbial-metabolic reactions to be used in biotechnological process

Modelling cellular metabolism is a strategic factor in investigating microbial behaviour and interactions, especially for bio-technological processes. A key factor for modelling microbial activity is the calculation of nutrient amounts and products generated as a result of the microbial metabolism. Representing metabolic pathways through balanced reactions is a complex and time-consuming task for biologists, ecologists, modellers and engineers. A new computational tool to represent microbial pathways through microbial metabolic reactions (MMRs) using the approach of the Thermodynamic Electron Equivalents Model has been designed and implemented in the open-access framework NetLogo. This computational tool, called MbT-Tool (Metabolism based on Thermodynamics) can write MMRs for different microbial functional groups, such as aerobic heterotrophs, nitrifiers, denitrifiers, methanogens, sulphate reducers, sulphide oxidizers and fermenters. The MbT-Tool's code contains eighteen organic and twenty inorganic reduction-half-reactions, four N-sources (NH4 (+), NO3 (-), NO2 (-), N2) to biomass synthesis and twenty-four microbial empirical formulas, one of which can be determined by the user (CnHaObNc). MbT-Tool is an open-source program capable of writing MMRs based on thermodynamic concepts, which are applicable in a wide range of academic research interested in designing, optimizing and modelling microbial activity without any extensive chemical, microbiological and programing experience.

Model development and experimental validation of capnophilic lactic fermentation and hydrogen synthesis by Thermotoga neapolitana

The aim of the present study was to develop a kinetic model for a recently proposed unique and novel metabolic process called capnophilic (CO2-requiring) lactic fermentation (CLF) pathway in Thermotoga neapolitana. The model was based on Monod kinetics and the mathematical expressions were developed to enable the simulation of biomass growth, substrate consumption and product formation. The calibrated kinetic parameters such as maximum specific uptake rate (k), semi-saturation constant (kS), biomass yield coefficient (Y) and endogenous decay rate (kd) were 1.30 h(-1), 1.42 g/L, 0.1195 and 0.0205 h(-1), respectively. A high correlation (>0.98) was obtained between the experimental data and model predictions for both model validation and cross validation processes. An increase of the lactate production in the range of 40-80% was obtained through CLF pathway compared to the classic dark fermentation model. The proposed kinetic model is the first mechanistically based model for the CLF pathway. This model provides useful information to improve the knowledge about how acetate and CO2 are recycled back by Thermotoga neapolitana to produce lactate without compromising the overall hydrogen yield.

INDISIM-Paracoccus, an individual-based and thermodynamic model for a denitrifying bacterium

We have developed an individual-based model for denitrifying bacteria. The model, called INDISIM-Paracoccus, embeds a thermodynamic model for bacterial yield prediction inside the individual-based model INDISIM, and is designed to simulate the bacterial cell population behavior and the product dynamics within the culture. The INDISIM-Paracoccus model assumes a culture medium containing succinate as a carbon source, ammonium as a nitrogen source and various electron acceptors such as oxygen, nitrate, nitrite, nitric oxide and nitrous oxide to simulate in continuous or batch culture the different nutrient-dependent cell growth kinetics of the bacterium Paracoccus denitrificans. The individuals in the model represent microbes and the individual-based model INDISIM gives the behavior-rules that they use for their nutrient uptake and reproduction cycle. Three previously described metabolic pathways for P. denitrificans were selected and translated into balanced chemical equations using a thermodynamic model. These stoichiometric reactions are an intracellular model for the individual behavior-rules for metabolic maintenance and biomass synthesis and result in the release of different nitrogen oxides to the medium. The model was implemented using the NetLogo platform and it provides an interactive tool to investigate the different steps of denitrification carried out by a denitrifying bacterium. The simulator can be obtained from the authors on request.

Identifying the Correct Biotransformation Model from Polychlorinated Biphenyl and Dioxin Dechlorination Batch Studies

We performed Monte Carlo simulations of batch transformations of hydrophobic compounds using typical numbers of data points, extent of reaction, and measurement error, to identify the most appropriate biotransformation model to describe such data under different conditions. Highly hydrophobic compounds such as polychlorinated biphenyls (PCBs) and dioxins present special challenges for parameterization due to low environmental concentrations and slow biotransformation rates, which result in high sample variability, few samples, and limited substrate concentration range. Four models of varying complexity (zero-order, first-order, Monod, and Best) were fit to simulated data. Various combinations of initial concentration (S0), half saturation concentration (KS), maximum substrate utilization rate (qmax), measurement error, number of data points per batch run, and extent of biotransformation were simulated. One thousand Monte-Carlo runs were performed for each parameter combination, and AICc (Akaike's information criterion corrected for small numbers of data points) was used to determine the most appropriate model. Neither the Best model nor the zero-order model ever produced the lowest AICc for a majority of simulations under any combination of test conditions. With 10% measurement error, the first-order model always outperformed the others. In the case of 1% measurement error with 10 evenly-spaced data points, the Monod model was the better choice when S0>KS and the system was not mass transfer limited [Formula: see text] otherwise, the first-order model was indicated. S0 is constrained by the compound's aqueous solubility; therefore, for highly hydrophobic compounds such as PCBs or polychlorinated dibenzo-p-dioxins and dibenzofurans, a first-order model is likely to fit batch biotransformation data as well or better than a more complicated model.

A new model for the biodegradation kinetics of oil droplets: application to the Deepwater Horizon oil spill in the Gulf of Mexico

Oil biodegradation by native bacteria is one of the most important natural processes that can attenuate the environmental impacts of marine oil spills. Existing models for oil biodegradation kinetics are mostly for dissolved oil. This work developed a new mathematical model for the biodegradation of oil droplets and applied the model to estimate the time scale for oil biodegradation under conditions relevant to the Deepwater Horizon oil spill in the Gulf of Mexico. In the model, oil is composed of droplets of various sizes following the gamma function distribution. Each oil droplet shrinks during the microbe-mediated degradation at the oil-water interface. Using our developed model, we find that the degradation of oil droplets typically goes through two stages. The first stage is characterized by microbial activity unlimited by oil-water interface with higher biodegradation rates than that of the dissolved oil. The second stage is governed by the availability of the oil-water interface, which results in much slower rates than that of soluble oil. As a result, compared to that of the dissolved oil, the degradation of oil droplets typically starts faster and then quickly slows down, ultimately reaching a smaller percentage of degraded oil in longer time. The availability of the water-oil interface plays a key role in determining the rates and extent of degradation. We find that several parameters control biodegradation rates, including size distribution of oil droplets, initial microbial concentrations, initial oil concentration and composition. Under conditions relevant to the Deepwater Horizon spill, we find that the size distribution of oil droplets (mean and coefficient of variance) is the most important parameter because it determines the availability of the oil-water interface. Smaller oil droplets with larger variance leads to faster and larger extent of degradation. The developed model will be useful for evaluating transport and fate of spilled oil, different remediation strategies, and risk assessment.

Performance evaluation and model analysis of BTEX contaminated air in corn-cob biofilter system

Biofiltration of BTEX with corn-cob packing material have been performed for a period of 68 days in five distinct phases. The overall performance of a biofilter has been evaluated in terms of its elimination capacity by using 3-D mesh techniques. Maximum removal efficiency was found more than 99.85% of all four compounds at an EBRT of 3.06 min in phase I for an inlet BTEX concentration of 0.0970, 0.0978, 0.0971 and 0.0968 g m(-3), respectively. Nearly 100% removal achieved at average BTEX loadings of 20.257 g m(-3) h(-1) to biofilter. A maximum elimination capacity (EC) of 20.239 g m(-3) h(-1) of the biofilter was obtained at inlet BTEX load of 20.391 g m(-3) h(-1). Moreover, using convection-diffusion reaction (CDR) model for biofilter depth shows good agreement with the experimental values for benzene, toluene and ethyl benzene, but for o-xylene the model results deviated from the experimental.

Insights into metabolic efficiency from flux analysis

The efficiency of carbon and energy flows throughout metabolism defines the potential for growth and reproductive success of plants. Understanding the basis for metabolic efficiency requires relevant definitions of efficiency as well as measurements of biochemical functions through metabolism. Here insights into the basis of efficiency provided by (13)C-based metabolic flux analysis (MFA) as well as the uses and limitations of efficiency in predictive flux balance analysis (FBA) are highlighted. (13)C-MFA studies have revealed unusual features of central metabolism in developing green seeds for the efficient use of light to conserve carbon and identified metabolic inefficiencies in plant metabolism due to dissipation of ATP by substrate cycling. Constraints-based FBA has used efficiency to guide the prediction of the growth and actual internal flux distribution of plant systems. Comparisons in a few cases have been made between flux maps measured by (13)C-based MFA and those predicted by FBA assuming one or more maximal efficiency parameters. These studies suggest that developing plant seeds and photoautotrophic microorganisms may indeed have patterns of metabolic flux that maximize efficiency. MFA and FBA are synergistic toolsets for uncovering and explaining the metabolic basis of efficiencies and inefficiencies in plant systems.

Thermodynamic electron equivalents model for bacterial yield prediction: modifications and comparative evaluations

Modifications are made to an earlier thermodynamic model (TEEM1) for prediction of maximum microbial yields from aerobic and anaerobic as well as heterotrophic and autotrophic growth. The revised model (TEEM2) corrects for lower yields found with aerobic oxidations of organic compounds where an oxygenase is involved and with growth on single-carbon (C1) compounds. TEEM1 and TEEM2 are based on energy release and consumption as determined from the reduction potential or Gibbs free energy of (1/2)-reaction reduction equations together with losses of energy during energy transfer. Energy transfer efficiency is a key parameter needed to make predictions with TEEM2, and was determined through evaluations with extensive data sets on aerobic heterotrophic yield available in the literature. For compounds following normal catabolic pathways, the best-fit value for energy transfer efficiency was 0.37, which permitted accurate predictions of growth with a precision of 15%-20% as determined by standard deviation. Using the same energy transfer efficiency, a similar precision, but somewhat less accuracy was found for organic compounds where oxidation involves an oxygenase (estimates 8% too high) and for C1 compounds (estimates 17% too high). In spite of the somewhat lower accuracy, the TEEM2 modifications resulted in improved predictions over TEEM1 and the comparison models.

Expanded thermodynamic true yield prediction model: adjustments and limitations

Bacterial yield prediction is critical for bioprocess optimization and modeling of natural biological systems. In previous work, an expanded thermodynamic true yield prediction model was developed through incorporating carbon balance and nitrogen balance along with electron balance and energy balance. In the present work, the application of the expanded model is demonstrated in multiple growth situations (aerobic heterotrophs, anoxic, anaerobic heterotrophs, and autolithotrophs). Two adjustments are presented that enable improved prediction when additional information regarding the environmental conditions (pH) or degradation pathway (requirement for oxygenase- or oxidase-catalyzed reactions) is known. A large data set of reported yields is presented and considered for suitability in model validation. Significant uncertainties of literature-reported yield values are described. Evaluation of the model with experimental yield values shows good predictive ability. However, the wide range in reported yields and the variability introduced into the prediction by uncertainty in model parameters, limits comprehensive validation. Our results suggest that the uncertainty of the experimental data used for validation limits further improvement of thermodynamic prediction models.

Flux analysis of central metabolic pathways in Geobacter metallireducens during reduction of soluble Fe(III)-nitrilotriacetic acid

We analyzed the carbon fluxes in the central metabolism of Geobacter metallireducens strain GS-15 using 13C isotopomer modeling. Acetate labeled in the first or second position was the sole carbon source, and Fe-nitrilotriacetic acid was the sole terminal electron acceptor. The measured labeled acetate uptake rate was 21 mmol/g (dry weight)/h in the exponential growth phase. The resulting isotope labeling pattern of amino acids allowed an accurate determination of the in vivo global metabolic reaction rates (fluxes) through the central metabolic pathways using a computational isotopomer model. The tracer experiments showed that G. metallireducens contained complete biosynthesis pathways for essential metabolism, and this strain might also have an unusual isoleucine biosynthesis route (using acetyl coenzyme A and pyruvate as the precursors). The model indicated that over 90% of the acetate was completely oxidized to CO2 via a complete tricarboxylic acid cycle while reducing iron. Pyruvate carboxylase and phosphoenolpyruvate (PEP) carboxykinase were present under these conditions, but enzymes in the glyoxylate shunt and malic enzyme were absent. Gluconeogenesis and the pentose phosphate pathway were mainly employed for biosynthesis and accounted for less than 3% of total carbon consumption. The model also indicated surprisingly high reversibility in the reaction between oxoglutarate and succinate. This step operates close to the thermodynamic equilibrium, possibly because succinate is synthesized via a transferase reaction, and the conversion of oxoglutarate to succinate is a rate-limiting step for carbon metabolism. These findings enable a better understanding of the relationship between genome annotation and extant metabolic pathways in G. metallireducens.

Bacterial growth yields on EDTA, NTA, and their biodegradation intermediates

Ethylenediaminetetraacetic acid (EDTA) and nitrilotriacetic acid (NTA) are widely used anthropogenic chelating agents for control of metal speciation and are ubiquitous in natural waters and wastewaters. This is the first report of systematic measurement of the growth yields of a mixed culture (BNC1-BNC2) on EDTA and its biodegradation intermediates, and of Aminobacter aminovorans (aka Chelatobacter heintzii) ATCC 29600 on NTA and its biodegradation intermediates. The yields measured for BNC1-BNC2 co-culture were 75.0 g of cell dry weight (CDW) (mole of EDTA)(-1), 68.6 g of CDW (mole of ED3 A)(-1), 51.2 g of CDW (mole of N,N'-EDDA)(-1), 34.5 g of CDW (mole of ED)(-1), 26.3 g of CDW (mole of IDA)(-1), 12.2 g of CDW (mole of glycine)(-1), and 9.7 g of CDW (mole of glyoxylate)(-1). The yields measured for A. aminovorans were 44.3 g of CDW (mole of NTA)(-1), 37.9 g of CDW (mole of IDA)(-1), 15.2 g of CDW (mole of glycine)(-1), and 10.4 g of CDW (mole of glyoxylate)(-1). The biodegradation pathways of EDTA, NTA, and several of their metabolic intermediates include reactions catalyzed by oxygenase enzymes, which may reduce energy available for cell synthesis. Comparison of measured yields with predicted yields indicates that the effect of oxygenase reaction on cell yield can be quantified experimentally as well as modeled based on thermodynamics.

A kinetic model describingShewanella oneidensis MR-1 growth, substrate consumption, and product secretion

Aerobic growth of Shewanella oneidensis MR-1 in minimal lactate medium was studied in batch cultivation. Acetate production was observed in the middle of the exponential growth phase and was enhanced when the dissolved oxygen (DO) concentration was low. Once the lactate was nearly exhausted, S. oneidensis MR-1 used the acetate produced during growth on lactate with a similar biomass yield as lactate. A two-substrate Monod model, with competitive and uncompetitive substrate inhibition, was devised to describe the dependence of biomass growth on lactate, acetate, and oxygen and the acetate growth inhibition across a broad range of concentrations. The parameters estimated for this model indicate interesting growth kinetics: lactate is converted to acetate stoichiometrically regardless of the DO concentration; cells grow well even at low DO levels, presumably due to a very low K(m) for oxygen; cells metabolize acetate (maximum specific growth rate, micro(max,A) of 0.28 h(-1)) as a single carbon source slower than they metabolize lactate (micro(max,L) of 0.47 h(-1)); and growth on acetate is self-inhibiting at a concentration greater than 10 mM. After estimating model parameters to describe growth and metabolism under six different nutrient conditions, the model was able to successfully estimate growth, oxygen and lactate consumption, and acetate production and consumption under entirely different growth conditions.