Biology subjects

Mönke, G.

Publications and source records attributed to Mönke, G..

4 recordsLinked to original sources

bioRxiv2024

Spectral estimation at the edge

Cognitive functions depend on neuronal communication, which is subserved by the synchronization of neuronal rhythms. Rhythms are characterized by their frequency, power and phase. If the phase of a rhythm just preceding an input is predictive of the neuronal or behavioral response to the input, this provides strong evidence for a functional role of the rhythm. Yet, this requires estimating the phase of a rhythm at the edge of the epoch. This is challenging, because any phase estimation that is spectrally specific requires a finite window length often combined with tapers that de-emphasize the signal close to the edge. To overcome this, we propose a method that builds on previously described approaches based on autoregressive modeling of the data and corresponding extrapolation beyond the edge. In contrast to related previous approaches, the modeling is based on the broadband signals, avoiding filtering-related group delays, and the extrapolation is performed multiple times, allowing averaging and thereby the reduction of extrapolation noise. The new method provided more accurate phase estimation at the edge for most simulated datasets, and for an empirical dataset from awake macaque area V4. We propose that the enhanced phase estimation accuracy at the edge might help to investigate the functional roles of brain rhythms and potentially also to improve phase-specific stimulation for clinical applications.

neuroscience↗

bioRxiv2024

Nonreciprocal synchronization in embryonic oscillator ensembles

Synchronization of coupled oscillators is a universal phenomenon encountered across different scales and contexts e.g., chemical wave patterns, superconductors and the unison applause we witness in concert halls. The existence of common underlying coupling rules define universality classes, revealing a fundamental sameness between seemingly distinct systems. Identifying rules of synchronization in any particular setting is hence of paramount relevance. Here, we address the coupling rules within an embryonic oscillator ensemble linked to vertebrate embryo body axis segmentation. In vertebrates, the periodic segmentation of the body axis involves synchronized signaling oscillations in cells within the presomitic mesoderm (PSM), from which somites, the pre-vertebrae, form. At the molecular level, it is known that intact Notch-signaling and cell-to-cell contact is required for synchronization between PSM cells. However, an understanding of the coupling rules is still lacking. To identify these, we develop a novel experimental assay that enables direct quantification of synchronization dynamics within mixtures of oscillating cell ensembles, for which the initial input frequency and phase distribution are known. Our results reveal a "winner-takes-it-all" synchronization outcome i.e., the emerging collective rhythm matches one of the input rhythms. Using a combination of theory and experimental validation, we develop a new coupling model, the "Rectified Kuramoto" (ReKu) model, characterized by a phase-dependent, non-reciprocal interaction in the coupling of oscillatory cells. Such non-reciprocal synchronization rules reveal fundamental similarities between embryonic oscillators and a class of collective behaviours seen in neurons and fireflies, where higher level computations are performed and linked to non-reciprocal synchronization.

systems biology↗

bioRxiv2020

Mechanical competition alters the cellular interpretation of an endogenous genetic programme

The intrinsic genetic programme of a cell is not sufficient to explain all of the cells activities. External mechanical stimuli are increasingly recognized as determinants of cell behaviour. In the epithelial folding event that constitutes the beginning of gastrulation in Drosophila, the genetic programme of the future mesoderm leads to the establishment of a contractile actomyosin network that triggers apical constriction of cells, and thereby, tissue folding. However, some cells do not constrict but instead stretch, even though they share the same genetic programme as their constricting neighbours. We show here that tissue-wide interactions force these cells to expand even when an otherwise sufficient amount of apical, active actomyosin is present. Models based on contractile forces and linear stress-strain responses do not reproduce experimental observations, but simulations in which cells behave as ductile materials with non-linear mechanical properties do. Our models show that this behaviour is a general emergent property of actomyosin networks [in a supracellular context, in accordance with our experimental observations of actin reorganisation within stretching cells.

developmental biology↗

bioRxiv2020

Optimal time frequency analysis for biological data - pyBOAT

Methods for the quantification 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 specifically designed to reveal time-dependent features. In this work we illustrate the advantages of our tool by analyzing complex non-stationary time-series profiles. Our approach integrates data-visualization, optimized sinc-filter detrending, amplitude envelope removal and a subsequent continuous-wavelet based time-frequency analysis. Finally, using analytical considerations and numerical simulations we discuss unexpected pitfalls in commonly used smoothing and detrending operations.

systems biology↗