Protein accumulation in the endoplasmic reticulum as a non-equilibrium phase transition

Several neurological disorders are associated with the aggregation of aberrant proteins, often localized in intracellular organelles such as the endoplasmic reticulum. Here we study protein aggregation kinetics by mean-field reactions and three dimensional Monte carlo simulations of diffusion-limited aggregation of linear polymers in a confined space, representing the endoplasmic reticulum. By tuning the rates of protein production and degradation, we show that the system undergoes a non-equilibrium phase transition from a physiological phase with little or no polymer accumulation to a pathological phase characterized by persistent polymerization. A combination of external factors accumulating during the lifetime of a patient can thus slightly modify the phase transition control parameters, tipping the balance from a long symptomless lag phase to an accelerated pathological development. The model can be successfully used to interpret experimental data on amyloid-\b{eta} clearance from the central nervous system

Frustration and thermalization in an artificial magnetic quasicrystal

Artificial frustrated systems offer a playground to study the emergent properties of interacting systems. Most work to date has been on spatially periodic systems, known as artificial spin ices when the interacting elements are magnetic. Here we have studied artificial magnetic quasicrystals based on quasiperiodic Penrose tiling patterns of interacting nanomagnets. We construct a low-energy configuration from a step-by-step approach that we propose as a ground state. Topologically induced emergent frustration means that this configuration cannot be constructed from vertices in their ground states. It has two parts, a quasi-one-dimensional ‘skeleton’ that spans the entire pattern and is capable of long-range order, surrounding ‘flippable’ clusters of macrospins that lead to macroscopic degeneracy. Magnetic force microscopy imaging of Penrose tiling arrays revealed superdomains that are larger for more strongly coupled arrays, especially after annealing the array above its blocking temperature

A network model for field and quenched disorder effects in artificial spin ice

We have performed a systematic study of the effects of field strength and quenched disorder on the driven dynamics of square artificial spin ice. We construct a network representation of the configurational phase space, where nodes represent the microscopic configurations and a directed link between node i and node j means that the field may induce a transition between the corresponding configurations. In this way, we are able to quantitatively describe how the field and the disorder affect the connectedness of states and the reversibility of dynamics. In particular, we have shown that for optimal field strengths, a substantial fraction of all states can be accessed using external driving fields, and this fraction is increased by disorder. We discuss how this relates to control and potential information storage applications for artificial spin ices

Dynamics and hysteresis in square lattice artificial spin-ice

Dynamical effects under geometrical frustration are considered in a model for artificial spin ice on a square lattice in two dimensions. Each island of the spin ice has a three-component Heisenberg-like dipole moment subject to shape anisotropies that influence its direction. The model has real dynamics, including rotation of the magnetic degrees of freedom, going beyond the Ising-type models of spin ice. The dynamics is studied using a Langevin equation solved via a second order Heun algorithm. Thermodynamic properties such as the specific heat are presented for different couplings. A peak in specific heat is related to a type of melting-like phase transition present in the model. Hysteresis in an applied magnetic field is calculated for model parameters where the system is able to reach thermodynamic equilibrium.Comment: Revised versio

Fluctuations in Protein Aggregation: Design of Preclinical Screening for Early Diagnosis of Neurodegenerative Disease

Autocatalytic fibril nucleation has recently been proposed to be a determining factor for the spread of neurodegenerative diseases, but the same process could also be exploited to amplify minute quantities of protein aggregates in a diagnostic context. Recent advances in microfluidic technology allow the analysis of protein aggregation in micron-scale samples, potentially enabling such diagnostic approaches, but the theoretical foundations for the analysis and interpretation of such data are, so far, lacking. Here, we study computationally the onset of protein aggregation in small volumes and show that the process is ruled by intrinsic fluctuations whose volume-dependent distribution we also estimate theoretically. Based on these results, we develop a strategy to quantify in silico the statistical errors associated with the detection of aggregate-containing samples. Our work explores a different perspective on the forecasting of protein aggregation in asymptomatic subjects

Extensive degeneracy, Coulomb phase and magnetic monopoles in an artificial realization of the square ice model

Artificial spin ice systems have been introduced as a possible mean to investigate frustration effects in a well-controlled manner by fabricating lithographically-patterned two-dimensional arrangements of interacting magnetic nanostructures. This approach offers the opportunity to visualize unconventional states of matter, directly in real space, and triggered a wealth of studies at the frontier between nanomagnetism, statistical thermodynamics and condensed matter physics. Despite the strong efforts made these last ten years to provide an artificial realization of the celebrated square ice model, no simple geometry based on arrays of nanomagnets succeeded to capture the macroscopically degenerate ground state manifold of the corresponding model. Instead, in all works reported so far, square lattices of nanomagnets are characterized by a magnetically ordered ground state consisting of local flux-closure configurations with alternating chirality. Here, we show experimentally and theoretically, that all the characteristics of the square ice model can be observed if the artificial square lattice is properly designed. The spin configurations we image after demagnetizing our arrays reveal unambiguous signatures of an algebraic spin liquid state characterized by the presence of pinch points in the associated magnetic structure factor. Local excitations, i.e. classical analogues of magnetic monopoles, are found to be free to evolve in a massively degenerated, divergence-free vacuum. We thus provide the first lab-on-chip platform allowing the investigation of collective phenomena, including Coulomb phases and ice-like physics.Comment: 26 pages, 10 figure

On thermalization of magnetic nano-arrays at fabrication

We propose a model to predict and control the statistical ensemble of magnetic degrees of freedom in Artificial Spin Ice (ASI) during thermalized adiabatic growth. We predict that as-grown arrays are controlled by the temperature at fabrication and by their lattice constant, and that they can be described by an effective temperature. If the geometry is conducive to a phase transition, then the lowest temperature phase is accessed in arrays of lattice constant smaller than a critical value, which depends on the temperature at deposition. Alternatively, for arrays of equal lattice constant, there is a temperature threshold at deposition and the lowest temperature phase is accessed for fabrication temperatures {\it larger rather than smaller} than this temperature threshold. Finally we show how to define and control the effective temperature of the as-grown array and how to measure critical exponents directly. We discuss the role of kinetics at the critical point, and applications to experiments, in particular to as-grown thermalized square ASI, and to magnetic monopole crystallization in as-grown honeycomb ASI.Comment: 14 pages, 2 figures. A theoretical approach to experimental results reported in: Morgan J P, Stein A, Langridge S and Marrows C (2010) Nature Physics 7 7

Metamaterial architecture from a self-shaping carnivorous plant

As meticulously observed and recorded by Darwin, the leaves of the carnivorous plant Drosera capensis L. slowly fold around insects trapped on their sticky surface in order to ensure their digestion. While the biochemical signaling driving leaf closure has been associated with plant growth hormones, how mechanical forces actuate the process is still unknown. Here, we combine experimental tests of leaf mechanics with quantitative measurements of the leaf microstructure and biochemistry to demonstrate that the closure mechanism is programmed into the cellular architecture of D. capensis leaves, which converts a homogeneous biochemical signal into an asymmetric response. Inspired by the leaf closure mechanism, we devise and test a mechanical metamaterial, which curls under homogeneous mechanical stimuli. This kind of metamaterial could find possible applications as a component in soft robotics and provides an example of bio-inspired design