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.
Highlights
The rise in chronic and progressive diseases worldwide leads to new challenges in the field of health economics (Nicholson, 2006)
In Section Integration of Longitudinal Omics Data: Methods and Challenges we address alternative methods currently used to overcome such complexity. 1986; Wold et al, 2001) and their derivates, such as Orthogonal Projection on Latent Structures (OPLS) (Trygg and Wold, 2002, 2003), are amongst the reference methodologies which perform well in low n, high p datasets through the projection of multivariate data onto a reduced subspace (Richards et al, 2010)
Methodologies able to adapt to the complexity of individual trajectories are needed, such as non-parametric statistical models, Generalized Estimating Equations (GEE), Markov models, Factor analysis and Bayesian models that have appeared as good tools for modeling longitudinal data
Summary
The rise in chronic and progressive diseases worldwide leads to new challenges in the field of health economics (Nicholson, 2006). As a pre-requisite, reference information on how dietary and lifestyle habits influence metabolic functions must be further expanded This will enable us to comprehensively document the biological processes associated with individual health at the different stages of the life cycle, including the critical pubertal physiological window, which may appear as a susceptibility period for several metabolic deregulations (Mantovani and Fucic, 2014). There is a need to adapt methodologies and design of experiment to explore processes related to growth, development, maturation and pubertal stages over months and years of the childhood spectrum The aim of this current review and perspective is to evaluate, summarily, some promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component (Table 1). We will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research
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