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Abstract Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically.

Background: (HSI) Hyperspectral Images contain high spectral resolution information, in hundreds of contiguous bands over a specific range of the electromagnetic spectrum. In science and industry, hyperspectral information is exploited by means of classification, anomaly and target detections algorithms. Specifically, in the last two decades a wide variety of hyperspectral target detection algorithms have been proposed. However, an optimal target detection algorithm with a remarkable performance over different kinds of targets and scenarios is still an active matter of research, due to the high spectral variability and diversity of real-world scenarios. Aim: This work presents a comparative study of target detection algorithms in hyperspectral imagery applied to agricultural crops in Colombia for evaluate performance in different scenarios. Method: The evaluations were performed on 20 real HSI acquired by the satellite Hyperion sensor, and 6 synthetic HSI with different noise levels. 5 synthetic targets were implemented; more than 115 spectral real signatures were extracted, 11 of those signatures were used as target in the testing process, allowing to characterize 5 agricultural crops of Colombian northeastern in 5 different areas. Results: The results show that the Adaptive Coherence Estimator (ACE) algorithm has a better performance in terms of detection probabilities PD > 90% for different scenarios and targets of agricultural type, in both synthetic and real images. Conclusions: In applications for target detection in HSI, it is critical to find an algorithm to have optimal performance for different scenarios and targets, due to the spectral variability generated by the geographical conditions countrywide. On the other hand, this work shows that is possible the development of new research fields and applications at the national level, taking advantage of hyperspectral imaging techniques for spectral detection, specifically for Colombian agriculture.

Título en ingles: Prediction of genome scale of Saccharomyces cerevisiae by flux balance analysis Resumen: El microorganismo Saccharomyces cerevisiae cuenta con gran número de modelos biológicos conocidos como reconstrucciones, las cuales pueden ser a escala genómica. De estas reconstrucciones a escala genómica provienen los modelos matemáticos, también llamados modelos estequiométricos. Una de las técnicas más usadas para estudiar estos modelos es el Análisis de Balance de Flujos (FBA). El proposito del FBA es predecir el crecimiento del microorganismo bajo estudio, y la producción y consumo de componentes como el etanol, CO2 glicerol, sucinato, acetato y piruvato. Para determinar si las predicciones obtenidas mediante FBA son únicas se utiliza la técnica de Análisis de Variabilidad Flujos (FVA). El presente trabajo muestra los resultados de aplicar el FBA a la reconstrucción reciente del microorganismo S. cerevisiae, la denominada iMM904 y los compara con un conjunto de datos experimentales presente en la literatura. Este trabajo también estudia la existencia de múltiples predicciones FBA utilizando la técnica FVA. Los resultados ilustran que es posible predecir el crecimiento del microorganimo S. cerevisiae, con errores entre el 11% y 28%; la producción de CO2, con errores entre el 0.3% y 4.5% y la producción de etanol, con errores entre el 11% y 13%. Palabras clave: analisis de balance de flujos, reconstrucción a escala genómica, iMM904, S. cerevisiae. Abstract: Several biological models, named reconstructions, are used for the study of the S. cerevisiae microorganism. The reconstructions can be genomic scaled. Mathematical models are generated from the reconstructions and they are called stoichiometric models. The flux balance analysis (FBA) is one of the tools used for the analysis of these models. The FBA attempts to predict the evolution of the microorganism and the consumption and production of components like glucose, ethanol, glycerol, succinate, acetate and pyruvate. A Flux variability analysis (FVA) is used to determine the uniqueness of the FBA predictions. This paper shows the results of applying FBA to the iMM904 reconstruction of S. cerevisiae and compares them with experimental data from literature. The results in this paper show that it is possible to predict the evolution with errors between 11% and 28% ; the production of CO2 with errors between 0.3% and 4.5%; and the production of ethanol with errors between 11% and 13%, using FBA for the iMM904 model. Keywords: flux balance analysis, genome scale reconstructions, iMM904, S. cerevisiae

Metabolic Flows Balance in Saccharomyces cerevisiae based on Intracellular Compartmentalization Titulo corto: Metabolic Flows Balance in Saccharomyces cerevisiae Resumen: 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. Palabras clave: Análisis de Balance de Flujos, FBA, iMM904, S. cerevisiae, Función Objetivo basada en Compartimentos. Abstract: Flux Balance Analysis - FBA - is one of the most used techniques in prediction of microorganism bioproducts. It requires an objective function that represents a biological objective for the microorganism studied. This paper presents a new kind of objective functions based on individual physical compartment objectives present in the microorganism studied. These kind of functions were tested with a stoichiometric model, extracted from iMM904 reconstruction of S. cerevisiae and its performance is compared with the objective function mainly used in literature, growth maximization, in anaerobic and aerobic batch conditions. The proposed objective function, in the present work, outperforms growth predictions in 10% and ethanol predictions in 75% as compared with the results obtained by maximization of growth objective function, in anaerobic conditions. In aerobic batch conditions, the suggested objective function outperform 98% in growth predictions and 70% in ethanol predictions in comparison with growth maximization. Key words: Flux Balance Analysis, FBA, iMM904 reconstruction, Compartment based Objective Function.

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.

Under ideal conditions, Escherichia coli cells divide after adding a fixed cell size, a strategy known as the adder. This concept applies to various microbes and is often explained as the division that occurs after a certain number of stages, associated with the accumulation of precursor proteins at a rate proportional to cell size. However, under poor media conditions, E. coli cells exhibit a different size regulation. They are smaller and follow a sizer-like division strategy where the added size is inversely proportional to the size at birth. We explore three potential causes for this deviation: degradation of the precursor protein and two models where the propensity for accumulation depends on the cell size: a nonlinear accumulation rate, and accumulation starting at a threshold size termed the commitment size. These models fit the mean trends but predict different distributions given the birth size. To quantify the precision of the models to explain the data, we used the Akaike information criterion and compared them to open datasets of slow-growing E. coli cells in different media. We found that none of the models alone can consistently explain the data. However, the degradation model better explains the division strategy when cells are larger, whereas size-related models (power-law and commitment size) account for smaller cells. Our methodology proposes a data-based method in which different mechanisms can be tested systematically. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Colombiana de Biotecnología is the property of Universidad Nacional de Colombia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)