Advancing from two core traits of biological systems: multilevel network organization and nonlinearity, we review a host of novel and readily available techniques to explore and analyze their complex dynamic behavior within the framework of experimental–computational synergy. In the context of concrete biological examples, analytical methods such as wavelet, power spectra, and metabolomics–fluxomics analyses, are presented, discussed, and their strengths and limitations highlighted. Further shown is how time series from stationary and nonstationary biological variables and signals, such as membrane potential, high-throughput metabolomics, O2 and CO2 levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal...