Enhanced Robustness of Calibration Models Using Parallel Factor (PARAFAC) Analysis with NIR Spectral Data for Non-invasive Blood Glucose Monitoring
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The main cause of the low prediction accuracy in non-invasive blood glucose monitoring with near-infrared (NIR) spectroscopy is that the variations induced by the changes of the measuring system in the prediction data set are inconsistent with those in the calibration data set.In this paper, a method to improve the robustness of the calibration models is proposed, in which the information of the matrix background is introduced as a variable into the calibration data and the three-way tensor is used to build the regression model.The idea of constructing regression models based on the hybrid algorithms consists of two steps.The first is to build a parallel factor (PARAFAC) model with its second-order advantage and calculate the scores and loadings.Then a multivariate linear regression (MLR) calibration model is built from the PARAFAC sample scores combined with the reference concentration values for quantification purposes.For the validation and prediction, the PARAFAC loadings are used to calculate the predicted scores with the validation and prediction data sets, and then the predicted concentration values can be deduced from the MLR model.The proposed method has been successfully applied to two NIR spectroscopy experiments.One is a Monte-Carlo simulation experiment of skin.The changes of the absorption coefficients and scattering coefficients of dermis are considered as the variations of the matrix background.The other is an in vitro experiment including glucose, haemoglobin and albumin solutions and the mixed composition solutions.The determination coefficients and root mean square error of prediction (RMSEP) values obtained from the PARAFAC-MLR models are compared with those obtained from traditional chemometrics tools such as partial least squares (PLS).The results show that the PLS model cannot handle uncalibrated variations whereas the way of introducing the matrix background to generate tensor data and the regression method based on the combination of PARAFAC and MLR perform better in model robustness and prediction precision.Keywords:
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It has long been recognized that robustness is an inherent property of all biological systems. For instance, microbes could maintain their intracellular environment in a relative stable level against the changes in the extracellular environment. We study such a phenomenon in the glycerol production by microbial continuous fermentation, in which the metabolism mechanisms are not completely known and the true metabolism system need to be identified from all possible ones. We present a quantitative index of biological robustness to measure the robustness of all candidate metabolic systems. An approximated estimation scheme for the calculation of the proposed robustness index is constructed and convergence result is presented.
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Abstract The application of Artificial Neural Networks (ANNs) for nonlinear multivariate calibration using simulated FTIR data was demonstrated in this paper. Neural networks consisting of three layers of nodes were trained by using the back-propagation learning rule. Since parameters affect the performance of the network greatly, simulated data were used to train the network in order to get a satisfactory combination of all parameters. The mixtures of four air toxic organic compounds whose FTIR spectra are overlapped were chosen to evaluate the calibration and prediction ability of the network. The relative standard error (RSD%), the percent standard error of prediction samples (%SEP) and the percent standard error of calibration samples (%SEC) are used for evaluating the ability of the neural network.
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The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility), but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.
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Spectroscopic methods are gaining in popularity in biotechnology because of their ability to deliver rapid, noninvasive measurements of the concentrations of multiple chemical species. Such measurements are particularly necessary for the implementation of control schemes for cell culture bioreactors. One of the major challenges to the development of spectroscopic methods for bioreactor monitoring is the generation of accurate and robust calibration models, particularly because of the inherent variability of biological processes. We have evaluated several methods of building calibration models, including synthetic calibrations and medium spiking methods. The approach that consistently produced reliable models incorporated samples removed from a bioreactor that were subsequently altered so as to increase the sample variation. Several large volume samples were removed from a bioreactor at varying time points and divided into multiple aliquots to which were added random, known amounts of the analytes of interest. Near-infrared spectra of these samples were collected and used to build calibration models. Such models were used to quantify analyte concentrations from independent samples removed from a second bioreactor. Prediction errors for alanine, glucose, glutamine, and leucine were 1.4, 1.0, 1.1, and 0.31 mM, respectively. This adaptive calibration method produces models with less error and less bias than observed with other calibration methods. Somewhat more accurate measurements could be attained with calibrations consisting of a combination of synthetic samples and spiked medium samples, but with an increase in calibration development time.
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We introduce a spatial model of concentration dynamics that supports the emergence of spatiotemporal inhomogeneities that engage in metabolism-boundary co-construction. These configurations exhibit disintegration following some perturbations, and self-repair in response to others. We define robustness as a viable configuration's tendency to return to its prior configuration in response to perturbations, and plasticity as a viable configuration's tendency to change to other viable configurations. These properties are demonstrated and quantified in the model, allowing us to map a space of viable configurations and their possible transitions. Combining robustness and plasticity provides a measure of viability as the average expected survival time under ongoing perturbation, and allows us to measure how viability is affected as the configuration undergoes transitions. The framework introduced here is independent of the specific model we used, and is applicable for quantifying robustness, plasticity, and viability in any computational model of artificial life that demonstrates the conditions for viability that we promote.
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Sensitivity of biochemical network models to uncertainties in the model structure, with a focus on autonomously oscillating systems, is addressed. Structural robustness, as defined here, concerns the sensitivity of the model predictions with respect to changes in the specific interactions between the network components and encompass, for instance, uncertain kinetic models, neglected intermediate reaction steps and unmodelled transport phenomena. Traditional parametric sensitivity analysis does not address such structural uncertainties and should therefore be combined with analysis of structural robustness. Here a method for quantifying the structural robustness of models for systems displaying sustained oscillations is proposed. The method adopts concepts from robust control theory and is based on adding dynamic perturbations to the network of interacting biochemical components. In addition to providing a measure of the overall robustness, the method is able to identify specific network fragilities. The importance of considering structural robustness is demonstrated through an analysis of a recently proposed model of the oscillatory metabolism in activated neutrophils. The model displays small parametric sensitivities, but is shown to be highly unrobust to small perturbations in some of the network interactions. Identification of specific fragilities reveals that adding a small delay or diffusion term in one of the involved reactions, likely to exist in vivo, completely removes all oscillatory behaviour in the model.
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Abstract The way that progenitor cell fate decisions and the associated environmental sensing are regulated to ensure the robustness of the spatial and temporal order in which cells are generated towards a fully differentiating tissue still remains elusive. Here, we investigate how cells regulate their sensing intensity and radius to guarantee the required thermodynamic robustness of a differentiated tissue. In particular, we are interested in finding the conditions where dedifferentiation at cell level is possible (microscopic reversibility) but tissue maintains its spatial order and differentiation integrity (macroscopic irreversibility). In order to tackle this, we exploit the recently postulated Least microEnvironmental Uncertainty Principle (LEUP) to develop a theory of stochastic thermodynamics for cell differentiation. To assess the predictive and explanatory power of our theory, we challenge it against the avian photoreceptor mosaic data. By calibrating a single parameter, the LEUP can predict the cone color spatial distribution in the avian retina and, at the same time, suggest that such a spatial pattern is associated with quasi-optimal cell sensing. By means of the stochastic thermodynamics formalism, we find out that thermodynamic robustness of differentiated tissues depends on cell metabolism and cell sensing properties. In turn, we calculate the limits of the cell sensing radius that ensure the robustness of differentiated tissue spatial order. Finally, we further constrain our model predictions to the avian photoreceptor mosaic.
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Variation in the concentration of biological components is inescapable for any cell. Robustness in any biological circuit acts as a cushion against such variation and enables the cells to produce homogeneous output despite the fluctuation. The two-component system (TCS) with a bifunctional sensor kinase (that possesses both kinase and phosphatase activities) is proposed to be a robust circuit. Few theoretical models explain the robustness of a TCS, although the criteria and extent of robustness by these models differ. Here, we provide experimental evidence to validate the extent of the robustness of a TCS signaling pathway. We have designed a synthetic circuit in Escherichia coli using a representative TCS of Mycobacterium tuberculosis, MprAB, and monitored the in vivo output signal by systematically varying the concentration of either of the components or both. We observed that the output of the TCS is robust if the concentration of MprA is above a threshold value. This observation is further substantiated by two in vitro assays, in which we estimated the phosphorylated MprA pool or MprA-dependent transcription yield by varying either of the components of the TCS. This synthetic circuit could be used as a model system to analyze the relationship among different components of gene regulatory networks.IMPORTANCE Robustness in essential biological circuits is an important feature of the living organism. A few pieces of evidence support the existence of robustness in vivo in the two-component system (TCS) with a bifunctional sensor kinase (SK). The assays were done under physiological conditions in which the SK was much lower than the response regulator (RR). Here, using a synthetic circuit, we varied the concentrations of the SK and RR of a representative TCS to monitor output robustness in vivo. In vitro assays were also performed under conditions where the concentration of the SK was greater than that of the RR. Our results demonstrate the extent of output robustness in the TCS signaling pathway with respect to the concentrations of the two components.
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Synthetic Biology
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