Compression-based inference of network motif sets.

Physical and functional constraints on biological networks lead to complex topological patterns across multiple scales in their organization. A particular type of higher-order network feature that has received considerable interest is network motifs, defined as statistically regular subgraphs. These may implement fundamental logical and computational circuits and are referred to as "building blocks of complex networks".

LarvaTagger: manual and automatic tagging of Drosophila larval behaviour.

Motivation: As more behavioural assays are carried out in large-scale experiments on Drosophila larvae, the definitions of the archetypal actions of a larva are regularly refined. In addition, video recording and tracking technologies constantly evolve. Consequently, automatic tagging tools for Drosophila larval behaviour must be retrained to learn new representations from new data.

Simulation-based inference for non-parametric statistical comparison of biomolecule dynamics.

Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment, out-of-equilibrium fluxes and conformal changes of the tracked molecules.

Variational inference of fractional Brownian motion with linear computational complexity.

We introduce a simulation-based, amortized Bayesian inference scheme to infer the parameters of random walks. Our approach learns the posterior distribution of the walks' parameters with a likelihood-free method. In the first step a graph neural network is trained on simulated data to learn optimized low-dimensional summary statistics of the random walk.

TRamWAy: mapping physical properties of individual biomolecule random motion in large-scale single-particle tracking experiments.

Motivation: Single-molecule localization microscopy allows studying the dynamics of biomolecules in cells and resolving the biophysical properties of the molecules and their environment underlying cellular function. With the continuously growing amount of data produced by individual experiments, the computational cost of quantifying these properties is increasingly becoming the bottleneck of single-molecule analysis. Mining these data requires an integrated and efficient analysis toolbox.

Hematopoietic stem cell transplantation chemotherapy causes microglia senescence and peripheral macrophage engraftment in the brain.

Hematopoietic stem cell transplantation (HSCT) is a therapy used for multiple malignant and nonmalignant diseases, with chemotherapy used for pretransplantation myeloablation. The post-HSCT brain contains peripheral engrafted parenchymal macrophages, despite their absence in the normal brain, with the engraftment mechanism still undefined. Here we show that HSCT chemotherapy broadly disrupts mouse brain regenerative populations, including a permanent loss of adult neurogenesis.

Statistical Tests for Force Inference in Heterogeneous Environments.

We devise a method to detect and estimate forces in a heterogeneous environment based on experimentally recorded stochastic trajectories. In particular, we focus on systems modeled by the heterogeneous overdamped Langevin equation. Here, the observed drift includes a "spurious" force term when the diffusivity varies in space.

Mapping spatio-temporal dynamics of single biomolecules in living cells.

We present a Bayesian framework for inferring spatio-temporal maps of diffusivity and potential fields from recorded trajectories of single molecules inside living cells. The framework naturally lets us regularise the high-dimensional inference problem using prior distributions in order to obtain robust results. To overcome the computational complexity of inferring thousands of map parameters from large single particle tracking datasets, we developed a stochastic optimisation method based on local mini-batches and parsimonious gradient calculation.

Correction: Temporal Gillespie algorithm: Fast simulation of contagion processes on time-varying networks.

[This corrects the article DOI: 10.1371/journal.pcbi.

Single-particle trajectories reveal two-state diffusion-kinetics of hOGG1 proteins on DNA.

We reanalyze trajectories of hOGG1 repair proteins diffusing on DNA. A previous analysis of these trajectories with the popular mean-squared-displacement approach revealed only simple diffusion. Here, a new optimal estimator of diffusion coefficients reveals two-state kinetics of the protein.

Recalibrating disease parameters for increasing realism in modeling epidemics in closed settings.

Background: The homogeneous mixing assumption is widely adopted in epidemic modelling for its parsimony and represents the building block of more complex approaches, including very detailed agent-based models. The latter assume homogeneous mixing within schools, workplaces and households, mostly for the lack of detailed information on human contact behaviour within these settings. The recent data availability on high-resolution face-to-face interactions makes it now possible to assess the goodness of this simplified scheme in reproducing relevant aspects of the infection dynamics.

Optimizing experimental parameters for tracking of diffusing particles.

We describe how a single-particle tracking experiment should be designed in order for its recorded trajectories to contain the most information about a tracked particle's diffusion coefficient. The precision of estimators for the diffusion coefficient is affected by motion blur, limited photon statistics, and the length of recorded time series. We demonstrate for a particle undergoing free diffusion that precision is negligibly affected by motion blur in typical experiments, while optimizing photon counts and the number of recorded frames is the key to precision.

Transition state theory demonstrated at the micron scale with out-of-equilibrium transport in a confined environment.

Transition state theory (TST) provides a simple interpretation of many thermally activated processes. It applies successfully on timescales and length scales that differ several orders of magnitude: to chemical reactions, breaking of chemical bonds, unfolding of proteins and RNA structures and polymers crossing entropic barriers. Here we apply TST to out-of-equilibrium transport through confined environments: the thermally activated translocation of single DNA molecules over an entropic barrier helped by an external force field.

Compensating for population sampling in simulations of epidemic spread on temporal contact networks.

Data describing human interactions often suffer from incomplete sampling of the underlying population. As a consequence, the study of contagion processes using data-driven models can lead to a severe underestimation of the epidemic risk. Here we present a systematic method to alleviate this issue and obtain a better estimation of the risk in the context of epidemic models informed by high-resolution time-resolved contact data.

Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.

Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks.

Harmonic force spectroscopy measures load-dependent kinetics of individual human β-cardiac myosin molecules.

Molecular motors are responsible for numerous cellular processes from cargo transport to heart contraction. Their interactions with other cellular components are often transient and exhibit kinetics that depend on load. Here, we measure such interactions using 'harmonic force spectroscopy'.

How memory generates heterogeneous dynamics in temporal networks.

Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks, little work has been devoted to the understanding of how such heterogeneities can emerge from microscopic mechanisms at the level of nodes and links. Here we show that long-term memory effects are present in the creation and disappearance of links in empirical networks.

Optimal estimation of diffusion coefficients from single-particle trajectories.

How does one optimally determine the diffusion coefficient of a diffusing particle from a single-time-lapse recorded trajectory of the particle? We answer this question with an explicit, unbiased, and practically optimal covariance-based estimator (CVE). This estimator is regression-free and is far superior to commonly used methods based on measured mean squared displacements. In experimentally relevant parameter ranges, it also outperforms the analytically intractable and computationally more demanding maximum likelihood estimator (MLE).

Filopodial retraction force is generated by cortical actin dynamics and controlled by reversible tethering at the tip.

Filopodia are dynamic, finger-like plasma membrane protrusions that sense the mechanical and chemical surroundings of the cell. Here, we show in epithelial cells that the dynamics of filopodial extension and retraction are determined by the difference between the actin polymerization rate at the tip and the retrograde flow at the base of the filopodium. Adhesion of a bead to the filopodial tip locally reduces actin polymerization and leads to retraction via retrograde flow, reminiscent of a process used by pathogens to invade cells.

Intracellular signaling by diffusion: can waves of hydrogen peroxide transmit intracellular information in plant cells?

Amplitude- and frequency-modulated waves of Ca(2+) ions transmit information inside cells. Reactive Oxygen Species (ROS), specifically hydrogen peroxide, have been proposed to have a similar role in plant cells. We consider the feasibility of such an intracellular communication system in view of the physical and biochemical conditions in plant cells.