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Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research.

Sperisen P, Cominetti O, Martin FP - Front Mol Biosci (2015)

Bottom Line: Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults.On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools.Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

View Article: PubMed Central - PubMed

Affiliation: GI Health and Microbiome Department, Nestle Institute of Health Sciences Lausanne, Switzerland.

ABSTRACT
Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

No MeSH data available.


Related in: MedlinePlus

Different levels of complexity in longitudinal omics data analysis. Schematic pictures depicting (A) a matrix with n number of subjects/samples and p number of analytes or variables measured, where n < p, (B) several matrices of same variables measured over time, where an increase in color gradient represents a change in time t; the variable corresponding to a given time point when the samples were collected or the measurements obtained, (C) two matrices of different platforms or variable types (e.g., metabolites and proteins) with different numbers of columns and (D) metabolic pathways where nodes correspond to metabolites and edges connecting the nodes correspond to enzymatic reactions. Different colors correspond to different metabolic pathways. In Section Integration of Longitudinal Omics Data: Methods and Challenges we address alternative methods currently used to overcome such complexity.
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Figure 1: Different levels of complexity in longitudinal omics data analysis. Schematic pictures depicting (A) a matrix with n number of subjects/samples and p number of analytes or variables measured, where n < p, (B) several matrices of same variables measured over time, where an increase in color gradient represents a change in time t; the variable corresponding to a given time point when the samples were collected or the measurements obtained, (C) two matrices of different platforms or variable types (e.g., metabolites and proteins) with different numbers of columns and (D) metabolic pathways where nodes correspond to metabolites and edges connecting the nodes correspond to enzymatic reactions. Different colors correspond to different metabolic pathways. In Section Integration of Longitudinal Omics Data: Methods and Challenges we address alternative methods currently used to overcome such complexity.

Mentions: Similarly to other omics technologies, metabolic profiling (Nicholson et al., 1999; Fiehn, 2002; Smith et al., 2006) based on mass spectrometric (MS) and nuclear magnetic resonance spectroscopy (NMR) produce data, analysis of which brings a number of challenges, with some requiring special attention in clinical omics studies, namely (i) high-dimensional nature of omics data; (ii) longitudinal aspect of multivariate omics data; (iii) multiple omics datasets; and (iv) mechanistic interpretation. The different levels of complexity are depicted through a series of schematic pictures in Figure 1. In the case of childhood metabolic health research these challenges are clearly present and important to address.


Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research.

Sperisen P, Cominetti O, Martin FP - Front Mol Biosci (2015)

Different levels of complexity in longitudinal omics data analysis. Schematic pictures depicting (A) a matrix with n number of subjects/samples and p number of analytes or variables measured, where n < p, (B) several matrices of same variables measured over time, where an increase in color gradient represents a change in time t; the variable corresponding to a given time point when the samples were collected or the measurements obtained, (C) two matrices of different platforms or variable types (e.g., metabolites and proteins) with different numbers of columns and (D) metabolic pathways where nodes correspond to metabolites and edges connecting the nodes correspond to enzymatic reactions. Different colors correspond to different metabolic pathways. In Section Integration of Longitudinal Omics Data: Methods and Challenges we address alternative methods currently used to overcome such complexity.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4525019&req=5

Figure 1: Different levels of complexity in longitudinal omics data analysis. Schematic pictures depicting (A) a matrix with n number of subjects/samples and p number of analytes or variables measured, where n < p, (B) several matrices of same variables measured over time, where an increase in color gradient represents a change in time t; the variable corresponding to a given time point when the samples were collected or the measurements obtained, (C) two matrices of different platforms or variable types (e.g., metabolites and proteins) with different numbers of columns and (D) metabolic pathways where nodes correspond to metabolites and edges connecting the nodes correspond to enzymatic reactions. Different colors correspond to different metabolic pathways. In Section Integration of Longitudinal Omics Data: Methods and Challenges we address alternative methods currently used to overcome such complexity.
Mentions: Similarly to other omics technologies, metabolic profiling (Nicholson et al., 1999; Fiehn, 2002; Smith et al., 2006) based on mass spectrometric (MS) and nuclear magnetic resonance spectroscopy (NMR) produce data, analysis of which brings a number of challenges, with some requiring special attention in clinical omics studies, namely (i) high-dimensional nature of omics data; (ii) longitudinal aspect of multivariate omics data; (iii) multiple omics datasets; and (iv) mechanistic interpretation. The different levels of complexity are depicted through a series of schematic pictures in Figure 1. In the case of childhood metabolic health research these challenges are clearly present and important to address.

Bottom Line: Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults.On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools.Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

View Article: PubMed Central - PubMed

Affiliation: GI Health and Microbiome Department, Nestle Institute of Health Sciences Lausanne, Switzerland.

ABSTRACT
Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.

No MeSH data available.


Related in: MedlinePlus