Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back

Abstract

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, O and CO levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal levels, can reveal important information on the properties of the underlying biological networks. Systems biology-inspired computational methods start to pave the way for addressing the integrated functional dynamics of metabolic, organelle and organ networks. As our capacity to unravel the control and regulatory properties of these networks and their dynamics under normal or pathological conditions broadens, so is our ability to address endogenous rhythms and clocks to improve health-span in human aging, and to manage complex metabolic disorders, neurodegeneration, and cancer. WIREs Syst Biol Med 2017, 9:e1352. doi: 10.1002/wsbm.1352 For further resources related to this article, please visit the WIREs website.

Diabetes Increases the Vulnerability of the Cardiac Mitochondrial Network to Criticality

“…Furthermore, the mean frequency in diabetic cells was lower with 18.3 ± 7.6 mHz (Figure 2E) than in control cells. This result indicates the presence of large clusters with predominant low-frequency, high-amplitude oscillations, likely resulting from relatively slow rate-controlling steps in the synchronization process (Kurz et al, 2010b(Kurz et al, , 2017. Interestingly, insulin treatment restores the frequency distribution to almost control levels, exhibiting a frequency bandwidth of (26.4 ± 10.4) mHz (Figure 2F).…”

“…To further examine and characterize the spatiotemporal dynamic behavior of the mitochondrial networks, we utilized grid-based wavelet analysis of cell fluorescence obtained by imaging with two-photon laser scanning microscopy. The grid-based wavelet approach enables monitoring of the time-dependent evolution of m oscillatory frequencies from mitochondria at both single and population levels, without the need to assume stationarity of the time series ( Supplementary Figures S2-S4; for Sham, STZ, and STZ + Ins, respectively) (Kurz et al, 2010a(Kurz et al, , 2017. Using an average TMRM-fluorescence image of the cell as a template (Supplementary Figure S2A, center), we created a hand-drawn grid (Supplementary Figure S2B, center).…”

“…The largest group of mitochondria with similar frequencies for a specific point in time constitutes a cluster exhibiting highly correlated and coherent m signal, involved in the macroscopic, cell-wide oscillations (Kurz et al, 2017). The size of the cluster influences synchronization; large clusters take longer to synchronize, thus their common frequency is lower compared to smaller clusters (Kurz et al, 2017).…”

Metabolic remodelling of glucose, fatty acid and redox pathways in the heart of type 2 diabetic mice

“…Systems biology computational methods have been pivotal to addressing the integrated functional dynamics of metabolic, organelle and cellular networks (Kurz et al . ). Metabolic disorders, cancer and caloric restriction, which have an impact on lifespan (Mitchell et al .…”

“…Systems biology computational methods have been pivotal to addressing the integrated functional dynamics of metabolic, organelle and cellular networks (Kurz et al 2017). Metabolic disorders, cancer and caloric restriction, which have an impact on lifespan (Mitchell et al 2016), together with pharmacological interventions able to modulate redox metabolism and healthspan (Mitchell et al 2018), are conspicuous examples of data-driven research that can directly benefit from quantitative knowledge of physiological and molecular mechanisms underlying their respective systemic states.…”