Thái Anh Nhan

Singularly perturbed integro-partial differential equations with reaction–diffusion behavior present significant challenges due to boundary layers arising from small perturbation parameters, which complicate the development of accurate and efficient numerical schemes for physical and engineering models. In this study, a uniformly convergent higher-order method is proposed to address these challenges. The approach applies the implicit Euler method for temporal discretization on a uniform mesh and central differences on a Shishkin mesh for spatial approximation, and utilizes the trapezoidal rule for evaluating integral terms; further, extrapolation techniques are incorporated in both time and space to increase accuracy. Numerical analysis demonstrates that the base scheme achieves first-order convergence, while extrapolation enhances convergence rates to second-order in time and fourth-order in space. Theoretical results confirm uniform convergence with respect to the perturbation parameter, and comprehensive numerical experiments validate these analytical claims. Findings indicate that the proposed scheme is reliable, efficient, and particularly effective in attaining fourth-order spatial accuracy when solving singularly perturbed integro-partial differential equations of reaction–diffusion type, thus providing a robust numerical tool for complex applications in science and engineering.

This article presents a fast and approximate multifrontal solver for large sparse linear systems. In a recent work by Liu et al., we showed the efficiency of a multifrontal solver leveraging the butterfly algorithm and its hierarchical matrix extension, HODBF (hierarchical off-diagonal butterfly) compression to compress large frontal matrices. The resulting multifrontal solver can attain quasi-linear computation and memory complexity when applied to sparse linear systems arising from spatial discretization of high-frequency wave equations. To further reduce the overall number of operations and especially the factorization memory usage to scale to larger problem sizes, in this article we develop a composite multifrontal solver that employs the HODBF format for large-sized fronts, a reduced-memory version of the nonhierarchical block low-rank format for medium-sized fronts, and a lossy compression format for small-sized fronts. This allows us to solve sparse linear systems of dimension up to 2.7 × larger than before and leads to a memory consumption that is reduced by 70% while ensuring the same execution time. The code is made publicly available in GitHub.

Enzymes are biological catalysts naturally present in living organisms, and they are capable of accelerating biochemical reactions in the metabolism process. Cells use many regulatory mechanisms to regulate the concentrations of cellular metabolites at physiological levels. Enzymatic inhibition is one of the key regulatory mechanisms naturally occurring in cellular metabolism, especially the enzymatic non-competitive inhibition by product. This inhibition process helps the cell regulate enzymatic activities. In this paper, we develop a novel mathematical model describing the enzymatic non-competitive inhibition by product. The model consists of a coupled system of nonlinear ordinary differential equations for the species of interest. Using nondimensionalization analysis, a formula for product formation rate for this mechanism is obtained in a transparent manner. Further analysis for this formula yields qualitative insights into the maximal reaction velocity and apparent Michaelis-Menten constant. Asymptotic solutions of the model are carefully given by using the homotopy perturbation analysis. A good agreement between the asymptotic solutions and numerical solutions are found. In addition, a Sobol global sensitivity analysis is implemented to help identify the key mechanisms of the enzyme activities. The results of this analysis show that the rate of product formation is relatively sensitive to the following factors: the catalytic rate of the enzyme, the rates of binding/unbinding of the product to/from the enzyme/enzyme complex. The numerical simulations provide insights into how variations in the model parameters affect the model output. Finally, an application of the model to the phosphorylation of glucose by mutant-hexokinase I enzyme is briefly discussed.

Enzymatic inhibition is one of the key regulatory mechanisms in cellular metabolism, especially the enzymatic competitive inhibition by product. This inhibition process helps the cell regulate enzymatic activities. In this paper, we derive a mathematical model describing the enzymatic competitive inhibition by product. The model consists of a coupled system of nonlinear ordinary differential equations for the species of interest. Using nondimensionalization analysis, a formula for product formation rate for this mechanism is obtained in a transparent manner. Further analysis for this formula yields qualitative insights into the maximal reaction velocity and apparent Michaelis-Menten constant. Integrating the model numerically, the effects of the model parameters on the model output are also investigated. Finally, a potential application of the model to realistic enzymes is briefly discussed.