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Natural gas is a mixture that contains hydrocarbons and other compounds, such as CO2 and N2. Natural gas composition is commonly measured by gas chromatography, and this measurement is important for the calculation of some thermodynamic properties that determine its commercial value. The estimation of uncertainty in chromatographic measurement is essential for an adequate presentation of the results and a necessary tool for supporting decision making. Various approaches have been proposed for the uncertainty estimation in chromatographic measurement. The present work is an evaluation of three approaches of uncertainty estimation, where two of them (guide to the expression of uncertainty in measurement method and prediction method) were compared with the Monte Carlo method, which has a wider scope of application. The aforementioned methods for uncertainty estimation were applied to gas chromatography assays of three different samples of natural gas. The results indicated that the prediction method and the guide to the expression of uncertainty in measurement method (in the simple version used) are not adequate to calculate the uncertainty in chromatography measurement, because uncertainty estimations obtained by those approaches are in general lower than those given by the Monte Carlo method.

RESUMO

Flux balance analysis (FBA) is currently one of the most important and used techniques for estimation of metabolic reaction rates (fluxes). This mathematical approach utilizes an optimization criterion in order to select a distribution of fluxes from the feasible space delimited by the metabolic reactions and some restrictions imposed over them, assuming that cellular metabolism is in steady state. Therefore, the obtained flux distribution depends on the specific objective function used. Multiple studies have been aimed to compare distinct objective functions at given conditions, in order to determine which of those functions produces values of fluxes closer to real data when used as objective in the FBA; in other words, what is the best objective function for modeling cell metabolism at a determined environmental condition. However, these comparative studies have been designed in very dissimilar ways, and in general, several factors that can change the ideal objective function in a cellular condition have not been adequately considered. Additionally, most of them have used only one dataset for representing one condition of cell growth, and different measuring techniques have been used. For these reasons, a rigorous study on the effect of factors such as the quantity of used data, the number and type of fluxes utilized as input data, and the selected classification of growth conditions, are required in order to obtain useful conclusions for these comparative studies, allowing limiting clearly the application range on any of those results.

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Una de las técnicas más utilizadas para la predicción de producción de bioproductos y distribución intracelular de flujos de microorganismos es el Análisis de Balance de Flujos - FBA por sus siglas en inglés. El FBA requiere de una función objetivo que represente el objetivo biológico del microorganismo estudiado. En este trabajo se propone un nuevo tipo de funciones objetivo basada en la combinación de objetivos de compartimentos físicos presentes en el microorganismo estudiado. Este tipo de funciones objetivo son examinadas junto con un modelo estequiométrico extraído de la reconstrucción iMM904 del microorganismo S. cerevisiae. Su desempeño se compara con la función objetivo más usada en la literatura, la maximización de biomasa, en condiciones experimentales anaeróbicas en cultivos continuos y aeróbicas en cultivos tipo lote. La función objetivo propuesta en este trabajo mejora las predicciones de crecimiento en un 10% y las predicciones de producción de etanol en un 75% respecto a las obtenidas por la función objetivo de maximización de biomasa, en condiciones anaeróbicas. En condiciones aeróbicas tipo lote la función objetivo propuesta mejora en un 98% las predicciones de crecimiento y en un 70% las predicciones de etanol con respecto a la función objetivo de biomasa.

Flux Balance Analysis - FBA - is one of the most used techniques in prediction of microorganism bioproducts. It requires an objective function that represents biological objective of the studied microorganism. This paper presents a new kind of objective functions based on individual physical compartment objetives in the studied microorganism. These kind of functions was tested with a stoichiometric model extracted from iMM904 reconstruction of S. cerevisiae and its performance is compared with the most used objective function in literature, growth maximization, in anaerobic and aerobic batch conditions. The presented objective function outperform growth predictions in 10% and ethanol predictions in 75% compared with obtained by maximization of growth objective function, in anaerobic conditions. In aerobic batch conditions the presented objective function outperforms in 98% growth preditions and 70% ethanol predictions compared with growth maximization.

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BACKGROUND: The main objective of flux balance analysis (FBA) is to obtain quantitative predictions of metabolic fluxes of an organism, and it is necessary to use an appropriate objective function to guarantee a good estimation of those fluxes. METHODOLOGY: In this study, the predictive performance of FBA was evaluated, using objective functions arising from the linear combination of different cellular objectives. This approach is most suitable for eukaryotic cells, owing to their multiplicity of cellular compartments. For this reason, Saccharomyces cerevisiae was used as model organism, and its metabolic network was represented using the genome-scale metabolic model iMM904. As the objective was to evaluate the predictive performance from the FBA using the kind of objective function previously described, substrate uptake and oxygen consumption were the only input data used for the FBA. Experimental information about microbial growth and exchange of metabolites with the environment was used to assess the quality of the predictions. CONCLUSIONS: The quality of the predictions obtained with the FBA depends greatly on the knowledge of the oxygen uptake rate. For the most of studied classifications, the best predictions were obtained with "maximization of growth", and with some combinations that include this objective. However, in the case of exponential growth with unknown oxygen exchange flux, the objective function "maximization of growth, plus minimization of NADH production in cytosol, plus minimization of NAD(P)H consumption in mitochondrion" gave much more accurate estimations of fluxes than the obtained with any other objective function explored in this study.

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