scholarly journals A Bayesian non-parametric mixed-effects model of microbial growth curves

2020 ◽  
Vol 16 (10) ◽  
pp. e1008366
Author(s):  
Peter D. Tonner ◽  
Cynthia L. Darnell ◽  
Francesca M. L. Bushell ◽  
Peter A. Lund ◽  
Amy K. Schmid ◽  
...  
Keyword(s):  
Population Growth ◽  
Random Effects ◽  
Growth Curves ◽  
Growth Conditions ◽  
Growth Behavior ◽  
Parametric Models ◽  
Growth Data ◽  
Growth Curve Models ◽  
Microbial Response ◽  
Non Parametric

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

scholarly journals A Bayesian Non-parametric Mixed-Effects Model of Microbial Phenotypes

2019 ◽  
Author(s):  
Peter D. Tonner ◽  
Cynthia L. Darnell ◽  
Francesca M.L. Bushell ◽  
Peter A. Lund ◽  
Amy K. Schmid ◽  
...  
Keyword(s):  
Population Growth ◽  
Random Effects ◽  
Growth Conditions ◽  
Growth Behavior ◽  
Parametric Models ◽  
Growth Data ◽  
Latent Growth ◽  
Growth Curve Models ◽  
Microbial Response ◽  
Non Parametric

AbstractSubstantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

scholarly journals Mixed-effects growth curves in the valuation of Nellore sires

2009 ◽  
Vol 66 (1) ◽  
pp. 84-89
Author(s):  
Suely Ruiz Giolo ◽  
Robin Henderson ◽  
Clarice Garcia Borges Demétrio
Keyword(s):  
Random Effects ◽  
Growth Curve ◽  
Production Systems ◽  
Growth Curves ◽  
Logistic Growth ◽  
Cattle Breeding ◽  
Growth Curve Models ◽  
Fixed And Random Effects ◽  
Curve Model ◽  
Breeding Programmes

Cattle breeding programmes need objective criteria in order to evaluate and subsequently improve production systems. This work uses a logistic growth curve model for evaluating sires based on their progeny weight measured repeatedly over time. The parameters of the curve are described as a linear function of fixed and random effects. A Bayesian approach is used for the estimation. Analysis of the weights recorded on animals of the Nellore breed shows that growth curve models with fixed and random effects can be useful to evaluate and selecting sires.

scholarly journals Detecting differential growth of microbial populations with Gaussian process regression

2016 ◽  
Author(s):  
Peter D Tonner ◽  
Cynthia L Darnell ◽  
Barbara E Engelhardt ◽  
Amy K Schmid
Keyword(s):  
Population Growth ◽  
Gaussian Process ◽  
Microbial Growth ◽  
Microbial Population ◽  
Statistical Tests ◽  
Growth Curves ◽  
Biological Knowledge ◽  
Growth Data ◽  
Differential Growth ◽  
Differential Effects

AbstractMicrobial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions that produce a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit non-standard growth curves. Rather than fix expected functional forms of different experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high resolution time-series growth data enables accurate quantification of population growth, and can be extended to identify differential growth phenotypes due to genetic background or stress. Additionally, confounding effects due to experimental variation can be controlled explicitly. Our framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model to a collection of growth measurements for seven transcription factor knockout strains of a model archaeal organism,Halobacterium salinarum. Using these models fitted to growth data, two statistical tests were developed to quantify the differential effects of genetic and environmental perturbations on microbial growth. These statistical tests accurately identify known regulators and implicate novel regulators of growth under standard and stress conditions. Furthermore, the fitted GP regression models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial population growth.

scholarly journals Bioactive Compounds, Antioxidant Activity and Growth Behavior in Lettuce Cultivars Grown under Field and Greenhouse Conditions

Proceedings ◽  
2020 ◽  
Vol 70 (1) ◽  
pp. 52
Author(s):  
Melisa Lanza Volpe ◽  
Verónica C. Soto Vargas ◽  
Anabel Morón ◽  
Roxana E. González
Keyword(s):  
Antioxidant Activity ◽  
Bioactive Compounds ◽  
Antioxidant Activities ◽  
Growth Conditions ◽  
Growth Behavior ◽  
Total Phenolic ◽  
Spectrophotometric Methods ◽  
Leafy Greens ◽  
Growing Conditions ◽  
Field Production

Lettuce (Lactuca sativa L.) is one of the most important leafy greens worldwide. The nutritional value of its edible leaf depends on different factors including type and growing conditions. The aim was to determine the bioactive compounds content, antioxidant activity and growth behavior of twenty-two lettuce genotypes, cultivated under field and greenhouse conditions. Total phenolic compound, chlorophylls, carotenoids, anthocyanin contents and antioxidant activities were analyzed by spectrophotometric methods. Data were analyzed by analysis of variance (ANOVA). Significant differences between bioactive compounds, antioxidant activity and growth behavior were found among cultivars and morphological types, for both growth conditions. Carotenoid and chlorophyll content was higher in greenhouse conditions for all genotypes. In field production, butterhead and iceberg lettuces showed lower content of these bioactive compounds. The red-pigmented Falbala cultivar from field production showed the highest level of polyphenols and anthocyanin. Meanwhile, in greenhouse conditions, the oak leaf cultivar Grenadine displayed the highest concentration of these phenolic compounds. The iceberg type lettuce showed the lowest percentages of antioxidant activity in both environments. The results showed the effect of growing conditions and the high variability in lettuce bioactive compounds content and antioxidant activity among the different types.

A Non Parametric Model for Magneto Rheological Dampers

1999 ◽  
Author(s):  
Mehdi Ahmadian ◽  
Xubin Song
Keyword(s):  
Test Data ◽  
Mr Damper ◽  
Parametric Model ◽  
Parametric Models ◽  
Magnetic Saturation ◽  
Mr Dampers ◽  
Electro Magnetic ◽  
Magneto Rheological ◽  
Force Characteristics ◽  
Non Parametric

Abstract A non-parametric model for magneto-rheological (MR) dampers is presented. After discussing the merits of parametric and non-parametric models for MR dampers, the test data for a MR damper is used to develop a non-parametric model. The results of the model are compared with the test data to illustrate the accuracy of the model. The comparison shows that the non-parametric model is able to accurately predict the damper force characteristics, including the damper non-linearity and electro-magnetic saturation. It is further shown that the parametric model can be numerically solved more efficiently than the parametric models.

Bayesian non-parametric models for regional prevalence estimation

2008 ◽  
Vol 35 (5) ◽  
pp. 567-582 ◽  
Author(s):  
Adam J. Branscum ◽  
Timothy E. Hanson ◽  
Ian A. Gardner
Keyword(s):  
Parametric Models ◽  
Prevalence Estimation ◽  
Non Parametric

Non-parametric lactation curves

1989 ◽  
Vol 48 (2) ◽  
pp. 331-339 ◽  
Author(s):  
D. A. Elston ◽  
C. A. Glasbey ◽  
D. R. Neilson
Keyword(s):  
Parametric Method ◽  
Parametric Models ◽  
Summary Statistics ◽  
Parametric Curves ◽  
Biased Estimation ◽  
Derivatives Of ◽  
Non Parametric

ABSTRACTLactation curves are fitted to data as a preliminary to estimating summary statistics. Two widely quoted curves are atbe-ct (Wood, 1967) and a(1 - e-bt) - ct (Cobby and Le Du, 1978), each of which has three parameters. Restriction to either of these curves imposes limitations on the fit to the data and can result in biased estimation of summary statistics. Alternatively, lactation curves can be generated by the use of a non-parametric method which requires only weak assumptions about the signs of derivatives of the curves. Because the non-parametric curves are more flexible, estimates of summary statistics are less likely to be biased than those based on parametric models. Use of the non-parametric curves is particularly advantageous around the time of peak yield, where the curves of Wood and Cobby and Le Du are known to fit data poorly.

scholarly journals Mathematical Models for Tumor Growth and the Reduction of Overtreatment

2018 ◽  
Vol 80 (01) ◽  
pp. 072-078 ◽  
Author(s):  
Berdine Heesterman ◽  
John-Melle Bokhorst ◽  
Lisa de Pont ◽  
Berit Verbist ◽  
Jean-Pierre Bayley ◽  
...  
Keyword(s):  
Tumor Growth ◽  
Mathematical Models ◽  
Goodness Of Fit ◽  
Mean Squared Error ◽  
Age At Onset ◽  
Growth Curves ◽  
Magnetic Resonance Images ◽  
Coefficient Of Determination ◽  
Growth Data ◽  
Significant Difference

Background To improve our understanding of the natural course of head and neck paragangliomas (HNPGL) and ultimately differentiate between cases that benefit from early treatment and those that are best left untreated, we studied the growth dynamics of 77 HNPGL managed with primary observation. Methods Using digitally available magnetic resonance images, tumor volume was estimated at three time points. Subsequently, nonlinear least squares regression was used to fit seven mathematical models to the observed growth data. Goodness of fit was assessed with the coefficient of determination (R 2) and root-mean-squared error. The models were compared with Kruskal–Wallis one-way analysis of variance and subsequent post-hoc tests. In addition, the credibility of predictions (age at onset of neoplastic growth and estimated volume at age 90) was evaluated. Results Equations generating sigmoidal-shaped growth curves (Gompertz, logistic, Spratt and Bertalanffy) provided a good fit (median R 2: 0.996–1.00) and better described the observed data compared with the linear, exponential, and Mendelsohn equations (p < 0.001). Although there was no statistically significant difference between the sigmoidal-shaped growth curves regarding the goodness of fit, a realistic age at onset and estimated volume at age 90 were most often predicted by the Bertalanffy model. Conclusions Growth of HNPGL is best described by decelerating tumor growth laws, with a preference for the Bertalanffy model. To the best of our knowledge, this is the first time that this often-neglected model has been successfully fitted to clinically obtained growth data.

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