F-Actin Bending Facilitates Net Actomyosin Contraction By Inhibiting Expansion With Plus-End-Located Myosin Motors

We investigate whether a microscopic system of two semi-flexible actin filaments with an attached myosin motor can facilitate contraction. Based on energy minimisation, we derive and analyse a partial differential equation model for a two-filament-motor structure embedded within a dense, two-dimensional network. Our method enables calculation of the plane stress tensor, providing a measure for contractility. After deriving the model, we use a combination of asymptotic analysis and numerical solutions to show how F-actin bending facilitates net contraction as a myosin motor traverses two symmetric filaments. Myosin motors close to the minus-ends facilitate contraction, whereas motors close to the plus-ends facilitate expansion. The leading-order solution for rigid filaments exhibits polarity-reversal symmetry, such that the contractile and expansive components balance to zero. Surprisingly, after introducing bending the first-order correction to stress indicates expansion. However, numerical solutions show that filament bending induces a geometric asymmetry that brings the filaments closer to parallel as a myosin motor approaches their plus-ends. This decreases the effective spring force opposing motion of the motor, enabling it to move faster close to filament plus-ends. This reduces the contribution of expansive stress, giving rise to net contraction. Further numerical solutions confirm that this applies beyond the small bending regime considered in the asymptotic analysis. Our findings confirm that filament bending gives rise to microscopic-scale actomyosin contraction, and provides a possible explanation for network-scale contraction.

Predicting Fluid Properties in the MUFITS Reservoir Simulator with User-Supplied Modules

We present a recent development of the MUFITS reservoir simulator aiming at modelling the transport of fluids whose properties and phase equilibria are calculated in a user-supplied external shared library. Both the explicit correlations and tabulated data for the fluid parameters can be implemented in the library that we name the EoS-module (Equation of State-module). An iterative approach—which, for example, is based on the phase equilibria calculation through the Gibbs energy minimisation (GEM) method, can also be used in the EoS-module. A considerable effort has been undertaken to minimise the number of program procedures exported by the shared library. This should facilitate and ease the usage of the developed software extension by the scientific community. Furthermore, we supplement the article with the source code of two simple EoS-modules that can serve as templates in other modelling and software development efforts. The EoS-modules are also useful for coupling MUFITS with other elaborate software for fluid property prediction. To demonstrate such a possibility, we supplement the article with the source code of a more complicated EoS-module that couples MUFITS with the geochemical code GEMS3K. This module is used in a simple 1-D benchmark study showing the capabilities of MUFITS for modelling reactive transport in porous media.

Is the brain an organ for free energy minimisation?

Two striking claims are advanced on behalf of the free energy principle (FEP) in cognitive science and philosophy: (i) that it identifies a condition of the possibility of existence for self-organising systems; and (ii) that it has important implications for our understanding of how the brain works, defining a set of process theories—roughly, theories of the structure and functions of neural mechanisms—consistent with the free energy minimising imperative that it derives as a necessary feature of all self-organising systems. I argue that the conjunction of claims (i) and (ii) rests on a fallacy of equivocation. The FEP can be interpreted in two ways: as a claim about how it is possible to redescribe the existence of self-organising systems (the Descriptive FEP), and as a claim about how such systems maintain their existence (the Explanatory FEP). Although the Descriptive FEP plausibly does identify a condition of the possibility of existence for self-organising systems, it has no important implications for our understanding of how the brain works. Although the Explanatory FEP would have such implications if it were true, it does not identify a condition of the possibility of existence for self-organising systems. I consider various ways of responding to this conclusion, and I explore its implications for the role and importance of the FEP in cognitive science and philosophy.

Is free-energy minimisation the mark of the cognitive?

A mark of the cognitive should allow us to specify theoretical principles for demarcating cognitive from non-cognitive causes of behaviour in organisms. Specific criteria are required to settle the question of when in the evolution of life cognition first emerged. An answer to this question should however avoid two pitfalls. It should avoid overintellectualising the minds of other organisms, ascribing to them cognitive capacities for which they have no need given the lives they lead within the niches they inhabit. But equally it should do justice to the remarkable flexibility and adaptiveness that can be observed in the behaviour of microorganisms that do not have a nervous system. We should resist seeking non-cognitive explanations of behaviour simply because an organism fails to exhibit human-like feats of thinking, reasoning and problem-solving. We will show how Karl Friston’s Free-Energy Principle (FEP) can serve as the basis for a mark of the cognitive that avoids the twin pitfalls of overintellectualising or underestimating the cognitive achievements of evolutionarily primitive organisms. The FEP purports to describe principles of organisation that any organism must instantiate if it is to remain well-adapted to its environment. Living systems from plants and microorganisms all the way up to humans act in ways that tend in the long run to minimise free energy. If the FEP provides a mark of the cognitive, as we will argue it does, it mandates that cognition should indeed be ascribed to plants, microorganisms and other organisms that lack a nervous system.

Melt migration by reactive porosity waves

<p>The formation of alkaline magmas observed worldwide requires that low degree-melts, potentially formed in the asthenosphere, were able to cross the overlying lithosphere. Fracturing in the upper, brittle part of the lithosphere may help to extract this melt to the surface. However, the mechanism of extraction in the lower, ductile part of the lithosphere is still contentious. Metasomatic enrichment of the lithospheric mantle demonstrates that such low-degree melts interact with the lithosphere, but the physical aspect of this process remains unclear.</p><p>Here, we aim to better understand, first, the percolation of magma in a porous viscous medium at pressure (P) and temperature (T) conditions relevant for the base of the lithosphere, and second, the impact of chemical differentiation on melt migration. We investigate theoretically the process of melt migration employing the fundamental laws of physics and thermodynamics. We simulate melt percolation numerically with a one-dimensional (1-D) Thermo-Hydro-Mechanical-Chemical (THMC) model of porosity waves coupled with thermodynamic results obtained from numerical Gibbs energy minimisation calculations. We perform THMC modelling and Gibbs energy minimisations with self-developed numerical algorithms using MATLAB and linear programming routines. We employ a simple ternary system of Forsterite/Fayalite/Enstatite for the solid and melt. Model variables, such as solid and melt densities or mass concentrations of MgO and SiO in solid and melt, are a function of pressure (P), temperature (T) and total silica concentration of the system (X). These variables are pre-computed with Gibbs energy minimisation and implemented in the THMC porosity wave transport code via parameterized equations, determining the T-P-X dependence of the model variables.</p><p>First results show that the total silica concentration and the temperature gradient are important parameters to consider in melt migration by reactive porosity waves. We discuss results of a systematic series of 1-D simulations and we present preliminary results form a 2-D reactive porosity wave model.</p>

Estimating the moments of a random forcing field of 2D fluid from image sequences using energy minimisation method

In this paper, we consider a version of energy minimisation technique applied to images of a 2D fluid flow. The two Navier–Stokes equations describe the static flow of a 2D fluid in terms of velocity field, (u, v), pressure field, p and forcing field, f. Apart from these two Navier–Stokes equations, we have the incompressibility condition to evaluate the three parameters. While implementing this system, random noise (usually non-Gaussian) creeps into the random force field $$\underline{f}(x,y)$$ f ̲ ( x , y ) . We denote this random field by $$\delta \underline{f}(x,y)$$ δ f ̲ ( x , y ) having zero mean and non-trivial second and third moments. We assume that these two moments are known except for some unknown parameters $$\underline{\theta }$$ θ ̲ (like mean, variance, co-variance, skewness, etc.) which we wish to estimate. In the proposed technique, we first calculate the approximate shift in the average fluid energy defined as a quadratic function of the velocity field. The energy method then requires that $$\underline{\theta }$$ θ ̲ should be such that this average increases in the energy due to the random forcing component be minimised. We should, however, note that the standard statistical approach to force field estimation is to calculate the velocity field as a function of the force field and then adopt the statistical moment matching technique. Such an approach assumes spatial ergodicity of the velocity field. This approach to force field estimation is more accurate from the statistical moment matching view point but works only if velocity measurements are made. The former technique of energy minimisation does not require any velocity measurements. Both of these techniques are discussed in this paper and MATLAB simulations presented.

Dynamical arrest of topological defects in 2D hyperuniform disk packings

We investigate collective motions of points in 2D systems, orchestrated by Lloyd algorithm. The algorithm iteratively updates a system by minimising the total quantizer energy of the Voronoi landscape of the system. As a result of a tradeoff between energy minimisation and geometric frustration, we find that optimised systems exhibit a defective landscape along the process, where strands of 5- and 7-coordinated dislocations are embedded in the hexatic phase. In particular, dipole defects, each of which is the simplest possible pair of a pentagon and a heptagon, come into the picture of dynamical arrest, as the system freezes down to a disordered hyperuniform state. Moreover, we explore the packing fractions of 2D disk packings associated to the obtained hyperuniform systems by considering the maximum inscribed disks in their Voronoi cells.