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

Cell contraction induces long-ranged stress stiffening in the extracellular matrix

Animal cells in tissues are supported by biopolymer matrices, which typically exhibit highly nonlinear mechanical properties. While the linear elasticity of the matrix can significantly impact cell mechanics and functionality, it remains largely unknown how cells, in turn, affect the nonlinear mechanics of their surrounding matrix. Here we show that living contractile cells are able to generate a massive stiffness gradient in three distinct 3D extracellular matrix model systems: collagen, fibrin, and Matrigel. We decipher this remarkable behavior by introducing Nonlinear Stress Inference Microscopy (NSIM), a novel technique to infer stress fields in a 3D matrix from nonlinear microrheology measurement with optical tweezers. Using NSIM and simulations, we reveal a long-ranged propagation of cell-generated stresses resulting from local filament buckling. This slow decay of stress gives rise to the large spatial extent of the observed cell-induced matrix stiffness gradient, which could form a mechanism for mechanical communication between cells

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

Actively crosslinked microtubule networks: mechanics, dynamics and filament sliding

Cytoskeletal networks are foundational examples of active matter and central to self-organized structures in the cell. In vivo, these networks are active and heavily crosslinked. Relating their large-scale dynamics to properties of their constituents remains an unsolved problem. Here we study an in vitro system made from microtubules and XCTK2 kinesin motors, which forms an aligned and active gel. Using photobleaching we demonstrate that the gel's aligned microtubules, driven by motors, continually slide past each other at a speed independent of the local polarity. This phenomenon is also observed, and remains unexplained, in spindles. We derive a general framework for coarse graining microtubule gels crosslinked by molecular motors from microscopic considerations. Using the microtubule-microtubule coupling, and force-velocity relationship for kinesin, this theory naturally explains the experimental results: motors generate an active strain-rate in regions of changing polarity, which allows microtubules of opposite polarities to slide past each other without stressing the material

Soft matter roadmap

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts

The variety of ordering transitions in liquids characterized by a locally favoured structure

We present a new lattice model of liquids in which the energy of a configuration is determined by the local coordination environments rather than pairwise interactions. This model is used to explore how the accumulation of order on cooling depends on the geometry of the locally favoured structure. We find that, while high-symmetry local structures result in ordering that occurs predominantly via a thermodynamic freezing transition, liquids characterised by a low-symmetry local structure exhibit a significant increase in local order on cooling before crystallizing

Learning the non-equilibrium dynamics of Brownian movies

Time-lapse microscopy imaging provides direct access to the dynamics of soft and living systems. At mesoscopic scales, such microscopy experiments reveal intrinsic thermal and non-equilibrium fluctuations. These fluctuations, together with measurement noise, pose a challenge for the dynamical analysis of these Brownian movies. Traditionally, methods to analyze such experimental data rely on tracking embedded or endogenous probes. However, it is in general unclear, especially in complex many-body systems, which degrees of freedom are the most informative about their non-equilibrium nature. Here, we introduce an alternative, tracking-free approach that overcomes these difficulties via an unsupervised analysis of the Brownian movie. We develop a dimensional reduction scheme selecting a basis of modes based on dissipation. Subsequently, we learn the non-equilibrium dynamics, thereby estimating the entropy production rate and time-resolved force maps. After benchmarking our method against a minimal model, we illustrate its broader applicability with an example inspired by active biopolymer gels

Morbidity of thyroid surgery.

BACKGROUND: Morbidity is today\u27s concern in thyroid surgery. The purpose of this paper was to quantify risk factors\u27 contribution to morbidity rates. METHODS: During 50 months, 1,163 patients undergoing 1,192 thyroidectomies at one hospital were reviewed at follow-up of 8 to 58 months. RESULTS: There was 1 death (0.08%). Wound morbidity included 19 hematomas (1.6%), 3 chyle leaks (0.2%), and 6 abscesses (0.5%). Mean hospital stay was 4.3 days after surgery without drain and 5.3 days with drain (P \u3c 0.01). Temporary and permanent hypoparathyroidism (TH; PH) rates were 20% and 4%. Parathyroid autografting and excision rates were 19% and 9%. TH rates were higher after parathyroid autografting or accidental excision (P \u3c 0.01). There was no correlation between the severity of TH and the number of lymph nodes at neck dissection nor between postoperative serum calcium levels and the number of parathyroids identified at bilateral surgery. Temporary and permanent recurrent laryngeal nerve (RLN) palsy (TRLNP; PRLNP) rates were 2.9% and 0.5% (0.3% of 2,010 RLNs at risk). PH and TRLNP (not PRLNP) rates were higher after completion or total thyroidectomy with node dissection (P \u3c 0.01). TRLNP and PRLNP rates after RLN exposure and after nonexposure were not statistically different. Surgical volume had no bearing on hematoma, abscess, TH, PH, TRLNP, and PRLNP rates. CONCLUSIONS: High surgical volume, identifying parathyroids and RLNs, failed to reduce morbidity. Completion and total thyroidectomy with node dissection increased PH and TRLNP (not PRLNP) rates