Systems biology has primarily focused on studying genomics, transcriptomics, and proteomics and their dynamic interactions. These, however, represent only the potential for a biological outcome since the ultimate phenotype at the level of the eventually produced metabolites is not taken into consideration. The emerging field of metabolomics provides complementary guidance toward an integrated approach to this problem: It allows global profiling of the metabolites of a cell, tissue, or host and presents information on the actual end points of a response. A wide range of data collection methods are currently used and allow the extraction of global or tissue-specific metabolic profiles. The great amount and complexity of data that are collected require multivariate analysis techniques, but the increasing amount of work in this field has made easy-to-use analysis programs readily available. Metabolomics has already shown great potential in drug toxicity studies, disease modeling, and diagnostics and may be integrated with genomic and proteomic data in the future to provide in-depth understanding of systems, pathways, and their functionally dynamic interactions. In this review we discuss the current state of the art of metabolomics, its applications, and future potential.

Monitoring the change in expression patterns over time provides the distinct possibility of unraveling the mechanistic drivers characterizing cellular responses. Gene arrays measuring the level of mRNA expression of thousands of genes simultaneously provide a method of high-throughput data collection necessary for obtaining the scope of data required for understanding the complexities of living organisms. Unraveling the coherent complex structures of transcriptional dynamics is the goal of a large family of computational methods aiming at upgrading the information content of time-course gene expression data. In this review, we summarize the qualitative characteristics of these approaches, discuss the main challenges that this type of complex data present, and, finally, explore the opportunities in the context of developing mechanistic models of cellular response.

Nutrient metabolism is under circadian regulation. Disruption of circadian rhythms by lifestyle and behavioral choices such as work schedules, eating patterns, and social jetlag, seriously impacts metabolic homeostasis. Metabolic dysfunction due to chronic misalignment of an organism's endogenous rhythms is detrimental to health, increasing the risk of obesity, metabolic and cardiovascular disease, diabetes, and cancer. In this paper, we review literature on recent findings on the mechanisms that communicate metabolic signals to circadian clocks and vice versa, and how human behavioral changes imposed by societal and occupational demands affect the physiological networks integrating peripheral clocks and metabolism. Finally, we discuss factors possibly contributing to inter-individual variability in response to circadian changes in the context of metabolic (dys)function.