Boundedness of a class of discretized reaction-diffusion systems
Jacqueline M. Wentz, David M. Bortz

TL;DR
This paper investigates conditions under which discretized reaction-diffusion systems with Neumann boundary conditions remain bounded over time, highlighting differences from continuous systems and introducing Lyapunov-like functions for analysis.
Contribution
It provides the first sufficient conditions for the boundedness of discretized reaction-diffusion systems with Neumann boundaries using Lyapunov-like functions.
Findings
Lyapunov-like functions guarantee boundedness of discretized systems.
Boundedness depends on the existence of specific Lyapunov-like functions.
Application to example systems illustrates when boundedness criteria are met or not.
Abstract
Although the spatially continuous version of the reaction-diffusion equation has been well studied, in some instances a spatially-discretized representation provides a more realistic approximation of biological processes. Indeed, mathematically the discretized and continuous systems can lead to different predictions of biological dynamics. It is well known in the continuous case that the incorporation of diffusion can cause diffusion-driven blow-up with respect to the norm. However, this does not imply diffusion-driven blow-up will occur in the discretized version of the system. For example, in a continuous reaction-diffusion system with Dirichlet boundary conditions and nonnegative solutions, diffusion-driven blow up occurs even when the total species concentration is non-increasing. For systems that instead have homogeneous Neumann boundary conditions, it is currently…
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
TopicsMathematical Biology Tumor Growth · Stability and Controllability of Differential Equations · Gene Regulatory Network Analysis
