Grey component analysis

Westerhuis, Johan A.; Derks, E.P.P.A.; Hoefsloot, Huub C. J.; Smilde, Age K.

doi:10.1002/cem.1072

Cited by 24 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the various sources of prior information have to be combined with the nutrikinetic signatures obtained from human interventions, for which we advocate the gray modeling approach. Gray modeling is a distribution free approach of Bayesian statistics where prior information is used and its confidence is expressed by a soft penalty (106). These gray models may be applied to find a relationship between the polyphenol intervention, the gut microbial levels, and the nutrikinetic signature of the individuals, in agreement with outcomes from in vitro and humanized mice experiments.…”

Section: Microbiome-metabolome Interactionsmentioning

confidence: 73%

Metabolic fate of polyphenols in the human superorganism

Duynhoven

Vaughan

Jacobs

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

466

330

View full text Add to dashboard Cite

Dietary polyphenols are components of many foods such as tea, fruit, and vegetables and are associated with several beneficial health effects although, so far, largely based on epidemiological studies. The intact forms of complex dietary polyphenols have limited bioavailability, with low circulating levels in plasma. A major part of the polyphenols persists in the colon, where the resident microbiota produce metabolites that can undergo further metabolism upon entering systemic circulation. Unraveling the complex metabolic fate of polyphenols in this human superorganism requires joint deployment of in vitro and humanized mouse models and human intervention trials. Within these systems, the variation in diversity and functionality of the colonic microbiota can increasingly be captured by rapidly developing microbiomics and metabolomics technologies. Furthermore, metabolomics is coming to grips with the large biological variation superimposed on relatively subtle effects of dietary interventions. In particular when metabolomics is deployed in conjunction with a longitudinal study design, quantitative nutrikinetic signatures can be obtained. These signatures can be used to define nutritional phenotypes with different kinetic characteristics for the bioconversion capacity for polyphenols. Bottom-up as well as top-down approaches need to be pursued to link gut microbial diversity to functionality in nutritional phenotypes and, ultimately, to bioactivity of polyphenols. This approach will pave the way for personalization of nutrition based on gut microbial functionality of individuals or populations.polyphenol bioconversion | gut microbiota | metabolomics | metagenomics | microbiomics

show abstract

Section: Microbiome-metabolome Interactionsmentioning

confidence: 73%

Metabolic fate of polyphenols in the human superorganism

Duynhoven

Vaughan

Jacobs

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

466

330

View full text Add to dashboard Cite

show abstract

“…the e↵ect of the substrates consumption of the cell or the environmental conditions in the production of a particular protein) are highly nonlinear or non-monotone. Recently, Grey Component Analysis (GCA) has been proposed using a cost function to maximise the interpretability of the solutions by forcing the decomposition towards the direction of the prior information -a chemically or biologically meaningful solution - [28]. A second strategy is formed by methods based on introducing a priori knowledge by means of mathematical relations that describe the system behaviour or dynamics.…”

Section: Introductionmentioning

confidence: 99%

Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media

González-Martínez

Folch-Fortuny

Llaneras

et al. 2014

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

ElsevierGonzález Martínez, JM.; Abel Folch-Fortuny; Llaneras Estrada, F.; Tortajada Serra, M.; Picó Marco, JA.; Ferrer, A. (2014 Within the emergent field of Systems Biology, mathematical models obtained from physical-chemical laws (the so-called first principles-based models) of microbial systems are employed to discern the principles that govern cellular behaviour and achieve a predictive understanding of cellular functions. The reliance on this biochemical knowledge has the drawback that some of the assumptions (specific kinetics of the reaction system, unknown dynamics and values of the model parameters) may not be valid for all the metabolic possible states of the network. In this uncertainty context, the combined use of fundamental knowledge and data measured in the fermentation that describe the behavior of the microorganism in the manufacturing process is paramount to overcome this problem. In this paper, a grey modelling approach is presented combining data-driven and first principles information at di↵erent scales, developed for Pichia pastoris cultures grown on di↵erent carbon sources. This approach will allow us to relate patterns of recombinant protein production to intracellular metabolic states and correlate intra and extracellular reactions in order to understand how the internal state of the cells determines the observed behaviour in P. pastoris cultivations.

show abstract

“…The focus of this overview is on multivariate calibration and/or maintenance with or without wavelength selection, but TR variants have been used in other ways in analytical chemistry. Briefly, TR‐themed approaches were used with self‐modeling curve resolution problems , converting raw measured spectra to derivative spectra ; a TR‐modified grey component analysis was developed for explorative data analysis ; and TR‐related approaches have been used for cancer classification with gene expression data .…”

Section: Introductionmentioning

confidence: 99%

Overview of two‐norm (L₂) and one‐norm (L₁) Tikhonov regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance

Kalivas

2012

Journal of Chemometrics

View full text Add to dashboard Cite

Building a multivariate calibration model is typically accomplished using partial least squares, principal component regression, or ridge regression, also derived as the standard form of Tikhonov regularization (TR). These approaches can be used in a full variable mode (full wavelengths for spectroscopic data) or with wavelength selection (bands and/or individual for sparse models). Calibration maintenance is an important aspect of multivariate calibration and describes the situation of maintaining acceptable predictions from a model over time. In terms of TR, this amounts to updating an existing model (determined under primary conditions) to handle new secondary conditions such as a new instrument, sample matrix, or environmental conditions. This paper overviews TR in its ability to form a primary calibration model or update a primary model to new secondary conditions while using full wavelengths or simultaneously selecting wavelengths to form respective sparse models. These objectives can be accomplished in two‐norm (L2) or one‐norm (L1) or combined formats. Also included is a TR design that minimizes the effect of the standardization set composition, i.e., reduces the effect of an outlier. Ongoing work that removes all reference samples from the TR framework is also described. Copyright © 2012 John Wiley & Sons, Ltd.

show abstract

Grey component analysis

Cited by 24 publications

References 27 publications

Metabolic fate of polyphenols in the human superorganism

Metabolic fate of polyphenols in the human superorganism

Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media

Overview of two‐norm (L₂) and one‐norm (L₁) Tikhonov regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance

Contact Info

Product

Resources

About

Grey component analysis

Cited by 24 publications

References 27 publications

Metabolic fate of polyphenols in the human superorganism

Metabolic fate of polyphenols in the human superorganism

Metabolic flux understanding of Pichia pastoris grown on heterogenous culture media

Overview of two‐norm (L2) and one‐norm (L1) Tikhonov regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance

Contact Info

Product

Resources

About

Overview of two‐norm (L₂) and one‐norm (L₁) Tikhonov regularization variants for full wavelength or sparse spectral multivariate calibration models or maintenance