Inferring the dynamics of underdamped stochastic systems

Many complex systems, ranging from migrating cells to animal groups, exhibit stochastic dynamics described by the underdamped Langevin equation. Inferring such an equation of motion from experimental data can provide profound insight into the physical laws governing the system. Here, we derive a principled framework to infer the dynamics of underdamped stochastic systems from realistic experimental trajectories, sampled at discrete times and subject to measurement errors. This framework yields an operational method, Underdamped Langevin Inference (ULI), which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems, including flocks with Viscek-like alignment interactions. Our method is robust to experimental measurement errors, and includes a self-consistent estimate of the inference error

Learning dynamical models of single and collective cell migration: a review

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualizing the emergent behavior of cells. We first review how this inference problem has been addressed in freely migrating cells on two-dimensional substrates and in structured, confining systems. Moreover, we discuss how data-driven methods can be connected with molecular mechanisms, either by integrating mechanistic bottom-up biophysical models, or by performing inference on subcellular degrees of freedom. Finally, we provide an overview of applications of data-driven modelling in developing frameworks for cell-to-cell variability in behaviours, and for learning the collective dynamics of multicellular systems. Specifically, we review inference and machine learning approaches to recover cell-cell interactions and collective dynamical modes, and how these can be integrated into physical active matter models of collective migration

Learning the dynamics of cell-cell interactions in confined cell migration

The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell-cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following and sliding past each other upon collision. Capitalizing on this large experimental data set of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting non-cancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and anti-friction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types

Geometry-driven migration efficiency of autonomous epithelial cell clusters

peer reviewe

Low Q^2 Jet Production at HERA and Virtual Photon Structure

The transition between photoproduction and deep-inelastic scattering is investigated in jet production at the HERA ep collider, using data collected by the H1 experiment. Measurements of the differential inclusive jet cross-sections dsigep/dEt* and dsigmep/deta*, where Et* and eta* are the transverse energy and the pseudorapidity of the jets in the virtual photon-proton centre of mass frame, are presented for 0 < Q2 < 49 GeV2 and 0.3 < y < 0.6. The interpretation of the results in terms of the structure of the virtual photon is discussed. The data are best described by QCD calculations which include a partonic structure of the virtual photon that evolves with Q2.Comment: 20 pages, 5 Figure

Hadron Production in Diffractive Deep-Inelastic Scattering

Characteristics of hadron production in diffractive deep-inelastic positron-proton scattering are studied using data collected in 1994 by the H1 experiment at HERA. The following distributions are measured in the centre-of-mass frame of the photon dissociation system: the hadronic energy flow, the Feynman-x (x_F) variable for charged particles, the squared transverse momentum of charged particles (p_T^{*2}), and the mean p_T^{*2} as a function of x_F. These distributions are compared with results in the gamma^* p centre-of-mass frame from inclusive deep-inelastic scattering in the fixed-target experiment EMC, and also with the predictions of several Monte Carlo calculations. The data are consistent with a picture in which the partonic structure of the diffractive exchange is dominated at low Q^2 by hard gluons.Comment: 16 pages, 6 figures, submitted to Phys. Lett.

Measurement of D* Meson Cross Sections at HERA and Determination of the Gluon Density in the Proton using NLO QCD

With the H1 detector at the ep collider HERA, D* meson production cross sections have been measured in deep inelastic scattering with four-momentum transfers Q^2>2 GeV2 and in photoproduction at energies around W(gamma p)~ 88 GeV and 194 GeV. Next-to-Leading Order QCD calculations are found to describe the differential cross sections within theoretical and experimental uncertainties. Using these calculations, the NLO gluon momentum distribution in the proton, x_g g(x_g), has been extracted in the momentum fraction range 7.5x10^{-4}< x_g <4x10^{-2} at average scales mu^2 =25 to 50 GeV2. The gluon momentum fraction x_g has been obtained from the measured kinematics of the scattered electron and the D* meson in the final state. The results compare well with the gluon distribution obtained from the analysis of scaling violations of the proton structure function F_2.Comment: 27 pages, 9 figures, 2 tables, submitted to Nucl. Phys.

Lasers and optics: Looking towards third generation gravitational wave detectors

Third generation terrestrial interferometric gravitational wave detectors will likely require significant advances in laser and optical technologies to reduce two of the main limiting noise sources: thermal noise due to mirror coatings and quantum noise arising from a combination of shot noise and radiation pressure noise. Increases in laser power and possible changes of the operational wavelength require new high power laser sources and new electro-optic modulators and Faraday isolators. Squeezed light can be used to further reduce the quantum noise while nano-structured optical components can be used to reduce or eliminate mirror coating thermal noise as well as to implement all-reflective interferometer configurations to avoid thermal effects in mirror substrates. This paper is intended to give an overview on the current state-of-the-art and future trends in these areas of ongoing research and development.NSF/PHY0555453NSF/PHY0757968NSF/PHY0653582DFG/SFB/407DFG/SFB/TR7DFG/EXC/QUES