Systems biology approaches to study the molecular effects of caloric restriction and polyphenols on aging processes

Abstract

Worldwide population is aging, and a large part of the growing burden associated with age-related conditions can be prevented or delayed by promoting healthy lifestyle and normalizing metabolic risk factors. However, a better understanding of the pleiotropic effects of available nutritional interventions and their influence on the multiple processes affected by aging is needed to select and implement the most promising actions. New methods of analysis are required to tackle the complexity of the interplay between nutritional interventions and aging, and to make sense of a growing amount of -omics data being produced for this purpose. In this paper, we review how various systems biology-inspired methods of analysis can be applied to the study of the molecular basis of nutritional interventions promoting healthy aging, notably caloric restriction and polyphenol supplementation. We specifically focus on the role that different versions of network analysis, molecular signature identification and multiomics data integration are playing in elucidating the complex mechanisms underlying nutrition, and provide some examples on how to extend the application of these methods using available microarray data.

Performance of methods that separate common and distinct variation in multiple data blocks

“…The JIVE method has gained popularity mainly in biomedical applications . The JIVE is, similar to DISCO, also an extension of the regular SCA decomposition and can be used for both multiblock and multiset applications.…”

Polyphenols and their benefits: A review

“…[299] Bioinformatics and systems biology approaches are helpful modern tools used to evaluate a large body of data associated with these phytochemicals. [4,306] Methods such as molecular docking, virtual screening, and molecular dynamics simulation, meanwhile, can provide a critical basis for understanding the complex behavior of these compounds in combination with animal enzymes.…”

Optimization Algorithms for Computational Systems Biology

“…Such selected properties, here generically called features, are then used to divide samples in categories. For example, respondent and nonrespondent to a particular drug or healthy and unhealthy [7,8]. Usually a sufficiently short list of features, called biomarker, is sought to discriminate the samples.…”

“…For example, they have been applied in synthetic and systems biology to determine biomarkers [7,8], design gene regulatory networks [76], and to estimate parameters [77]. In addition, the convergence of certain GAs has been formally proved for problems involving only discrete parameters [17,18], and some steps in providing bounds for the runtime have been done [78,79].…”

“…At each iteration, the selection occurs as a weighted random choice among the candidate solutions, where individuals with higher fitness are more likely to be selected (Algorithm 3, lines [7][8]. At this stage, selected individuals are cloned in a new pool of candidates, called intermediate population (Algorithm 3, line 9).…”