Existence and uniqueness of solution of the differential equation describing the TASEP-LK coupled transport process
Jingwei Li, Yunxin Zhang

TL;DR
This paper rigorously proves the existence and uniqueness of solutions for the differential equations modeling the TASEP-LK coupled transport process, providing a solid mathematical foundation for previous numerical and simulation studies.
Contribution
It introduces a mathematical analysis establishing the existence and uniqueness of solutions for the TASEP-LK differential equations, which was previously only supported by numerical evidence.
Findings
Existence of steady state solutions confirmed
Uniqueness of solutions in C∞ space established
Time-dependent solutions shown to exist and be unique in space Xη
Abstract
In this paper, the existence and uniqueness of solution of a specific differential equation is studied. This equation originates from the description of a coupled process by totally asymmetric simple exclusion process (TASEP) and Langmuir kinetics (LK). In the fields of physics and biology, the properties of the TASEP-LK coupled process have been extensively studied by Monte Carlo simulations and numerical calculations, as well as detailed experiments. However, so far, no rigorous mathematical analysis has been given to the corresponding differential equations, especially their existence and uniqueness of solution. In this paper, using the upper and lower solution method, the existence of solution of the steady state equation is obtained. Then using a generalized maximum principle, we show that the solution constructed from the upper and lower solution method is actually the unique…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and statistical mechanics · Diffusion and Search Dynamics · Markov Chains and Monte Carlo Methods
