Search

Synchronization occurs in many natural and technological systems, from cardiac pacemaker cells to coupled lasers. In the synchronized state, the individual cells or lasers coordinate the timing of their oscillations, but they do not move through space. A complementary form of self-organization occurs among swarming insects, flocking birds, or schooling fish; now the individuals move through space, but without conspicuously altering their internal states. Here we explore systems in which both synchronization and swarming occur together. Specifically, we consider oscillators whose phase dynamics and spatial dynamics are coupled. We call them swarmalators, to highlight their dual character. A case study of a generalized Kuramoto model predicts five collective states as possible long-term modes of organization. These states may be observable in groups of sperm, Japanese tree frogs, colloidal suspensions of magnetic particles, and other biological and physical systems in which self-assembly and synchronization interact., Collective self-organized behavior can be observed in a variety of systems such as colloids and microswimmers. Here O’Keeffe et al. propose a model of oscillators which move in space and tend to synchronize with neighboring oscillators and outline five types of collective self-organized states.

Inference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may overlook the simultaneous and reciprocal nature of causal interactions observed in real world phenomena. Here, we present a causal decomposition approach that is not based on prediction, but based on the instantaneous phase dependency between the intrinsic components of a decomposed time series. The method involves two assumptions: (1) any cause effect relationship can be quantified with instantaneous phase dependency between the source and target decomposed as intrinsic components at specific time scale, and (2) the phase dynamics in the target originating from the source are separable from the target itself. Using empirical mode decomposition, we show that the causal interaction is encoded in instantaneous phase dependency at a specific time scale, and this phase dependency is diminished when the causal-related intrinsic component is removed from the effect. Furthermore, we demonstrate the generic applicability of our method to both stochastic and deterministic systems, and show the consistency of the causal decomposition method compared to existing methods, and finally uncover the key mode of causal interactions in both the modelled and actual predator prey system. We anticipate that this novel approach will assist with revealing causal interactions in complex networks not accounted for by current methods., 5 figures

Intrinsically disordered regions (IDRs) are often fast-evolving protein domains of low sequence complexity that can drive phase transitions and are commonly found in many proteins associated with neurodegenerative diseases, including the RNA processing factor TDP43. Yet, how phase separation contributes to the physiological functions of TDP43 in cells remains enigmatic. Here, we combine systematic mutagenesis guided by evolutionary sequence analysis with a live-cell reporter assay of TDP43 phase dynamics to identify regularly-spaced hydrophobic motifs separated by flexible, hydrophilic segments in the IDR as a key determinant of TDP43 phase properties. This heuristic framework allows customization of the material properties of TDP43 condensates to determine effects on splicing function. Remarkably, even a mutant that fails to phase-separate at physiological concentrations can still efficiently mediate the splicing of a quantitative, single-cell splicing reporter and endogenous targets. This suggests that the ability of TDP43 to phase-separate is not essential for its splicing function., TDP43 regulates RNA processing and undergoes liquid-liquid phase separation. Here, the authors combine bioinformatic analysis and quantitative measurements of phase properties in living cells to uncover the rules that link the amino acid sequence of TDP43 with its phase properties and find that unexpectedly, mutants of TDP43 that are deficient in phase separation can still function to mediate splicing.

Since the pioneering proposal of the replicon model of DNA replication 50 years ago, the predicted replicons have not been identified and quantified at the cellular level. Here, we combine conventional and super-resolution microscopy of replication sites in live and fixed cells with computational image analysis. We complement these data with genome size measurements, comprehensive analysis of S-phase dynamics and quantification of replication fork speed and replicon size in human and mouse cells. These multidimensional analyses demonstrate that replication foci (RFi) in three-dimensional (3D) preserved somatic mammalian cells can be optically resolved down to single replicons throughout S-phase. This challenges the conventional interpretation of nuclear RFi as replication factories, that is, the complex entities that process multiple clustered replicons. Accordingly, 3D genome organization and duplication can be now followed within the chromatin context at the level of individual replicons., Whether replication happens at individual replicons or in replication factories is a controversial debate. Here the authors use super-resolution microscopy and analysis of replication fork speed to present evidence in favour of replicons.