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We introduce a mathematical model for the mechanical behaviour of the eukaryotic cell cytoskeleton. This discrete model involves a regular array of pre-stressed protein filaments that exhibit resistance to enthalpic stretching, joined at…

Quantitative Methods · Quantitative Biology 2023-09-06 J. Köry , N. A. Hill , X. Y. Luo , P. S. Stewart

Physics-Informed Neural Networks (PINNs) have aroused great attention for its ability to address forward and inverse problems of partial differential equations. However, approximating discontinuous functions by neural networks poses a…

Computational Engineering, Finance, and Science · Computer Science 2024-09-10 Luyang Zhao , Qian Shao

In this paper we present a network model to study the impact of spatial distribution of constituents, coupling between them and diffusive processes in the context of biological situations. The model is in terms of network of mobile elements…

Molecular Networks · Quantitative Biology 2007-05-23 Kanchan Thadani , Ashutosh

The stochastic differential equations for a model of dissipative particle dynamics, with both total energy and total momentum conservation at every time-step, are presented. The algorithm satisfies detailed balance as well as the…

Statistical Mechanics · Physics 2007-05-23 Allan D. Mackie , Josep Bonet Avalos

The introduction of Physics-informed Neural Networks (PINNs) has led to an increased interest in deep neural networks as universal approximators of PDEs in the solid mechanics community. Recently, the Deep Energy Method (DEM) has been…

Computational Engineering, Finance, and Science · Computer Science 2022-01-26 Jan N. Fuhg , Nikolaos Bouklas

In this work, the Cosserat formulation of geometrically exact beam dynamics is extended by adding the electric potential as an additional degree of freedom to account for the electromechanical coupling in the Dielectric Elastomer Actuators…

Computational Engineering, Finance, and Science · Computer Science 2021-12-01 Dengpeng Huang , Sigrid Leyendecker

In this project, we present a deep neural network (DNN)-based biophysics model that uses multi-scale and uniform topological and electrostatic features to predict protein properties, such as Coulomb energies or solvation energies. The…

Machine Learning · Computer Science 2026-03-16 Elyssa Sliheet , Md Abu Talha , Weihua Geng

Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies amongst the spatial and temporal components of a wide variety of dynamical…

Optimization and Control · Mathematics 2021-08-04 Sarthak Chatterjee , Andrea Alessandretti , A. Pedro Aguiar , Sérgio Pequito

The paper addresses a common assumption of elastoplastic modeling: that the recoverable, elastic strain increment is unaffected by alterations of the elastic moduli that accompany loading. This assumption is found to be false for a granular…

Soft Condensed Matter · Physics 2018-12-20 Matthew R. Kuhn , Ali Daouadji

Presented here is a study of well-posedness and asymptotic stability of a "degenerately damped" PDE modeling a vibrating elastic string. The coefficient of the damping may vanish at small amplitudes thus weakening the effect of the…

We propose a criterion for optimal parameter selection in coarse-grained models of proteins, and develop a refined elastic network model (ENM) of bovine trypsinogen. The unimodal density-of-states distribution of the trypsinogen ENM…

Biomolecules · Quantitative Biology 2009-09-29 Dengming Ming , Michael Wall

Quite recently I have proposed a nonperturbative dynamical effective field model (DEFM) to quantitatively describe the dynamics of interacting ferrofluids. Its predictions compare very well with the results from simulations. In this paper I…

Soft Condensed Matter · Physics 2020-11-18 Angbo Fang

The problem of quasistatic and rate-independent evolution of elastic-plastic-brittle delamination at small strains is considered. Delamination processes for linear elastic bodies glued by an adhesive to each other or to a rigid outer…

Numerical Analysis · Mathematics 2012-12-12 C. G. Panagiotopoulos , V. Mantic , T. Roubicek

We investigate proteins within heterogeneous cell membranes where non-equilibrium phenomena arises from spatial variations in concentration and temperature. We develop simulation methods building on non-equilibrium statistical mechanics to…

Soft Condensed Matter · Physics 2025-08-28 D. Jasuja , P. J. Atzberger

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g. the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic…

Molecular Networks · Quantitative Biology 2009-11-13 Gautier Stoll , Jacques Rougemont , Felix Naef

Networks of elastic beams can deform either by stretching or bending of their members. The primary mode of deformation (bending or stretching) crucially depends on the specific details of the network architecture. In order to shed light on…

Soft Condensed Matter · Physics 2016-09-23 Gérald Gurtner , Marc Durand

A model system inspired by recent experiments on the dynamics of a folded protein under the influence of a sinusoidal force is investigated and found to replicate many of the response characteristics of such a system. The essence of the…

Soft Condensed Matter · Physics 2015-09-30 Craig Fogle , Joseph Rudnick , David Jasnow

A flexible fiber model based on the discrete element method (DEM) is presented and validated for the simulation of uniaxial compression of flexible fibers in a cylindrical container. It is found that the contact force models in the DEM…

Soft Condensed Matter · Physics 2023-01-18 Yu Guo , Yanjie Li , Qingzhao Liu , Zhenhua Li , Hanhui Jin , Carl Wassgren , Jennifer S. Curtis

Energy-Based Models (EBMs) have proven to be a highly effective approach for modelling densities on finite-dimensional spaces. Their ability to incorporate domain-specific choices and constraints into the structure of the model through…

Machine Learning · Computer Science 2023-02-24 Jen Ning Lim , Sebastian Vollmer , Lorenz Wolf , Andrew Duncan

It is the purpose of the present article to show that so-called network models, originally designed to describe static properties of disordered electronic systems, can be easily generalized to quantum-{\em dynamical} models, which then…

Disordered Systems and Neural Networks · Physics 2015-06-25 Rochus Klesse , Marcus Metzler