The value of systems biology in cardiology is becoming more recognized. There has been a tremendous rise in the number of articles in the last two decades, as publicly available datasets have been provided online and high-throughput tissue analysis has become more prevalent. In animal models, however, the future of cardiovascular medicine is less likely to be reanalyzing data and more likely to be investigating the function of GWAS-identified SNPs or network change using informatics and gene-editing technologies. These techniques, when combined with other omics interrogations and rigorous experimental design, have the potential to improve our understanding of gene-to-disease pathways.Systems biology is a method for studying large amounts of multidimensional data generated by omics technologies and, more broadly, the transition to big data in health care.Cross-validation of the various technological platforms is critical because omics studies are prone to bias and overinterpretation.Investigators must carefully determine which publicly accessible datasets, if any, to employ while conducting a systems analysis. Despite the fact that network theory and machine learning may yield amazing outcomes, these methods are not yet standardized. The studies mentioned here are excellent examples, in part because they use empirical models to support emergent systems biology results. In the few successful cases, careful experimental design, including interventional research and clinical trials, is required, in addition to the insights supplied by bioinformatics analysis of omics approaches. While it may be tempting to use emergent qualities to capture these new discoveries in more fundamental concepts, we agree with the English philosopher William of Ockham when he says, "It is futile to do with more things what can be done with fewer."
THE EFFECTS OF A VIRTUAL LABORATORY AND META-COGNITIVE SCAFFOLDING ON STUDENTS' DATA MODELING COMPETENCES
