257 research outputs found

    A class of robust numerical methods for solving dynamical systems with multiple time scales

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    In this paper, we develop a class of robust numerical methods for solving dynamical systems with multiple time scales. We first represent the solution of a multiscale dynamical system as a transformation of a slowly varying solution. Then, under the scale separation assumption, we provide a systematic way to construct the transformation map and derive the dynamic equation for the slowly varying solution. We also provide the convergence analysis of the proposed method. Finally, we present several numerical examples, including ODE system with three and four separated time scales to demonstrate the accuracy and efficiency of the proposed method. Numerical results verify that our method is robust in solving ODE systems with multiple time scale, where the time step does not depend on the multiscale parameters

    A Novel Stochastic Interacting Particle-Field Algorithm for 3D Parabolic-Parabolic Keller-Segel Chemotaxis System

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    We introduce an efficient stochastic interacting particle-field (SIPF) algorithm with no history dependence for computing aggregation patterns and near singular solutions of parabolic-parabolic Keller-Segel (KS) chemotaxis system in three space dimensions (3D). The KS solutions are approximated as empirical measures of particles coupled with a smoother field (concentration of chemo-attractant) variable computed by the spectral method. Instead of using heat kernels causing history dependence and high memory cost, we leverage the implicit Euler discretization to derive a one-step recursion in time for stochastic particle positions and the field variable based on the explicit Green's function of an elliptic operator of the form Laplacian minus a positive constant. In numerical experiments, we observe that the resulting SIPF algorithm is convergent and self-adaptive to the high gradient part of solutions. Despite the lack of analytical knowledge (e.g. a self-similar ansatz) of the blowup, the SIPF algorithm provides a low-cost approach to study the emergence of finite time blowup in 3D by only dozens of Fourier modes and through varying the amount of initial mass and tracking the evolution of the field variable. Notably, the algorithm can handle at ease multi-modal initial data and the subsequent complex evolution involving the merging of particle clusters and formation of a finite time singularity

    A class of robust numerical methods for solving dynamical systems with multiple time scales

    Get PDF
    In this paper, we develop a class of robust numerical methods for solving dynamical systems with multiple time scales. We first represent the solution of a multiscale dynamical system as a transformation of a slowly varying solution. Then, under the scale separation assumption, we provide a systematic way to construct the transformation map and derive the dynamic equation for the slowly varying solution. We also provide the convergence analysis of the proposed method. Finally, we present several numerical examples, including ODE system with three and four separated time scales to demonstrate the accuracy and efficiency of the proposed method. Numerical results verify that our method is robust in solving ODE systems with multiple time scale, where the time step does not depend on the multiscale parameters

    Structural Properties and Interaction Partners of Familial ALS-Associated SOD1 Mutants

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    Amyotrophic lateral sclerosis (ALS) is the most common motor neuron degenerative disease in adults and has also been proven to be a type of conformational disease associated with protein misfolding and dysfunction. To date, more than 150 distinct genes have been found to be associated with ALS, among which Superoxide Dismutase 1 (SOD1) is the first and the most extensively studied gene. It has been well-established that SOD1 mutants-mediated toxicity is caused by a gain-of-function rather than the loss of the detoxifying activity of SOD1. Compared with the clear autosomal dominant inheritance of SOD1 mutants in ALS, the potential toxic mechanisms of SOD1 mutants in motor neurons remain incompletely understood. A large body of evidence has shown that SOD1 mutants may adopt a complex profile of conformations and interact with a wide range of client proteins. Here, in this review, we summarize the fundamental conformational properties and the gained interaction partners of the soluble forms of the SOD1 mutants which have been published in the past decades. Our goal is to find clues to the possible internal links between structural and functional anomalies of SOD1 mutants, as well as the relationships between their exposed epitopes and interaction partners, in order to help reveal and determine potential diagnostic and therapeutic targets
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