No abstract

Methods for the quantication of rhythmic biological signals have been essential for the discovery of function and design of biological oscillators. Advances in live measurements have allowed recordings of unprecedented resolution revealing a new world of complex heterogeneous oscillations with multiple noisy non-stationary features. However, our understanding of the underlying mechanisms regulating these oscillations has been lagging behind, partially due to the lack of simple tools to reliably quantify these complex non-stationary features. With this challenge in mind, we have developed pyBOAT, a Python-based fully automatic stand-alone software that integrates multiple steps of non-stationary oscillatory time series analysis into an easy-to-use graphical user interface. pyBOAT implements continuous wavelet analysis which is specically designed to reveal time-dependent features. In this work we illustrate the advantages of our tool by analyzing complex non-stationary time-series proles. Our approach integrates data-visualization, optimized sinc-lter detrending, amplitude envelope removal and a subsequent continuous-wavelet based timefrequency analysis. Finally, using analytical considerations and numerical simulations we discuss unexpected pitfalls in commonly used smoothing and detrending operations.

Cellular signaling systems precisely transmit information in the presence of molecular noise while retaining flexibility to accommodate the needs of individual cells. To understand design principles underlying such versatile signaling, we analyzed the response of the tumor suppressor p53 to varying levels of DNA damage in hundreds of individual cells and observed a switch between distinct signaling modes characterized by isolated pulses and sustained oscillations of p53 accumulation. Guided by dynamic systems theory we show that this requires an excitable network structure comprising positive feedback and provide experimental evidence for its molecular identity. The resulting data-driven model reproduced all features of measured signaling responses and is sufficient to explain their heterogeneity in individual cells. We present evidence that heterogeneity in the levels of the feedback regulator Wip1 sets cell-specific thresholds for p53 activation, providing means to modulate its response through interacting signaling pathways. Our results demonstrate how excitable signaling networks can provide high specificity, sensitivity and robustness while retaining unique possibilities to adjust their function to the physiology of individual cells.

While many tumors initially respond to chemotherapy, regrowth of surviving cells compromises treatment efficacy in the long term. The cell-biological basis of this regrowth is not understood. Here, we characterize the response of individual, patient-derived neuroblastoma cells driven by the prominent oncogene MYC to the first-line chemotherapy, doxorubicin. Combining live-cell imaging, cell-cycle-resolved transcriptomics, and mathematical modeling, we demonstrate that a cell's treatment response is dictated by its expression level of MYC and its cell-cycle position prior to treatment. All low-MYC cells enter therapy-induced senescence. High-MYC cells, by contrast, disable their cell-cycle checkpoints, forcing renewed proliferation despite treatment-induced DNA damage. After treatment, the viability of high-MYC cells depends on their cell-cycle position during treatment: newborn cells promptly halt in G phase, repair DNA damage, and form re-growing clones; all other cells show protracted DNA repair and ultimately die. These findings demonstrate that fast-proliferating tumor cells may resist cytotoxic treatment non-genetically, by arresting within a favorable window of the cell cycle.